devtools::source_url("https://github.com/jjcurtin/lab_support/blob/main/fun_ml.R?raw=true") # for conflicts
options(conflicts.policy = "depends.ok")
tidymodels_conflictRules()Unit 12: Natural Language Processing, Text Processing, and Feature Engineering
Setup
Handle conflicts
Load packages
library(tidyverse)
library(tidymodels)
library(tokenizers) # you wont need this one loaded in the future as it is wrapped by other functions
library(tidytext)
library(textrecipes) # step_* functions for NLP
library(data.table, include.only = "fread")
library(kableExtra, exclude = "group_rows")Source function scripts (John’s or your own)
devtools::source_url("https://github.com/jjcurtin/lab_support/blob/main/fun_eda.R?raw=true") # for tab functionSpecify other global settings
theme_set(theme_classic())
options(tibble.width = Inf, dplyr.print_max=Inf)
rerun_setting <- FALSECreate file path object
path_data <- "2025/application_assignments/unit_12"Set up parallel processing
Note you can type cl into your console to see how many cores your computer has.
cl <- parallel::makePSOCKcluster(parallel::detectCores(logical = FALSE))
doParallel::registerDoParallel(cl)Read in Data
data <- read_csv(here::here(path_data, "alcohol_tweets.csv"), col_types = cols()) |>
mutate(user_drinking = factor(user_drinking)) |>
glimpse()Rows: 4,944
Columns: 3
$ id <dbl> 4.717116e+17, 4.645540e+17, 4.680948e+17, 4.703539e+17, …
$ user_drinking <fct> no, yes, no, yes, no, no, no, no, no, no, no, no, yes, y…
$ text <chr> "\"@ShigDollaz: Shot Dollaz We Got Back Wit Ya! Fuck Yo …For this assignment it is helpful to see (and potentially copy) the id values from tweets, which we can’t do easily if the number is in scientific notation. To change this in R, set the relevant option called scipen. You can do this by running the code below, which says that any integer with 20 decimal places or fewer, will be in raw integer form, not scientific notation. This can be useful with ID-type variables like we use here.
options(scipen = 20)Glove embeddings
Here we read in the data directly from the web. If we downloaded the file maunally, we could read it in using fread() as shown below because it’s very large.
temp <- tempfile()
options(timeout = max(300, getOption("timeout"))) # need more time to download big file
download.file("https://nlp.stanford.edu/data/glove.6B.zip", temp)
unzip(temp, files = "glove.6B.50d.txt")
glove_embeddings <- read_delim(here::here(path_data, "glove.6B.50d.txt"),
delim = " ",
col_names = FALSE) Rows: 8 Columns: 51
── Column specification ────────────────────────────────────────────────────────
Delimiter: " "
chr (1): X1
dbl (50): X2, X3, X4, X5, X6, X7, X8, X9, X10, X11, X12, X13, X14, X15, X16,...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#glove_embeddings <- fread(here::here(path_data, 'glove_twitter.csv')) |>
# as_tibble()We can make our validation splits now. Here, we are subsetting our data to create data_train based on the indices generated from the split. This is a handy use of slice() to remember. Note that we can’t use the code we normally use to get data_train because it doesn’t work with validation_split
set.seed(12345)
splits <- data |>
validation_split(strata = "user_drinking")Warning: `validation_split()` was deprecated in rsample 1.2.0.
ℹ Please use `initial_validation_split()` instead.#Pull out indices of training data
training_ind <- splits |>
unlist(recursive = FALSE)
training_ind <- training_ind$splits$in_id
data_train <- data |>
slice(training_ind)Part 1: Cleaning EDA
First let’s examine a few tweets to get familiar with the data. Here is one that is about the user drinking, the positive class.
data_train |> filter(user_drinking == 'yes') |>
slice(1:10) |>
pull(text) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| x |
|---|
| Chris is my DD while I drink by myself Wednesday afternoon and get WASTED celebrating the end of this semester.. #seniorbarcrawl ? |
| N/A |
| I need to turn up tonight! 🍹🍷🍻 |
| There's nothing like my own bed after 3 bottles or so of wine. #winedrunk #happydrunk #thursday |
| N/A |
| Still drunk 😖 |
| Drinking a Jack Daniel's Berry Punch by Jack Daniel's Beverage Co. @ Party City — http://t.co/JSqyPyRbgr |
| N/A |
| #WIDN listening to @Coldplay play live at the Beacon Theatre (thx @SiriusXM!) w/ a glass of wine + @lushcosmetics http://t.co/FdGYYdem84 |
| N/A |
This is an example where the user isn’t drinking (but note that they still reference alcohol).
# not drinking
data_train |> filter(user_drinking == 'no') |>
slice(1:10) |>
pull(text) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| x |
|---|
| "@ShigDollaz: Shot Dollaz We Got Back Wit Ya! Fuck Yo Leg Up Now You Will Never Run Again"ayyy |
| Straight from the airport. When in Rome... er, Rochester! (@ Dinosaur Bar-B-Que - @dinobbq) http://t.co/s14oEOirvf |
| @joey_mondoo Honestly I wouldn't be surprised if they won or lost the series. I would be OK with defeat, but the Nets do have a good shot. |
| Welcome to Sunnyside Lodge at Black Creek Park, the home of my graduation party June 28th! http://t.co/eq59vLM0rw |
| "@NatTheBeautiful: Our generation jus got life fucked up period"deadass |
| N/A |
| N/A |
| N/A |
| @kidkozay I know haha it's funny watching him continueing to go right and keep getting stopped then just chucking up a shot |
| Yonce all on his mouth like liquor |
Check on the balance of our outcome.
data_train |> tab(user_drinking)# A tibble: 2 × 3
user_drinking n prop
<fct> <int> <dbl>
1 no 2175 0.587
2 yes 1533 0.413It is somewhat balanced (which is an artifact of the sampling process, discussed below). One thing we notice is that the “not drinking” tweets still seem to contain some drinking words (e.g., “party”).
Some background on these data: all tweets were first filtered to contain alcohol keywords (e.g., “party”, “beer”, etc). So this data set is not a good representation of how our model would do in the wild (where we would encounter far more tweets unrelated to drinking, that would contain different language). This also helps explain why the outcome is so much more balanced then it would be!
Next, some cleaning to consider: links, emojis, hashtags, and N/A. Lets start by tokenizing just to see what we get. First, set a few example tweets with each to compare what happens to them during tokenization.
# tweet with emojis
emoji_id = 467354410902110208
data_train |> filter(id == emoji_id) |> pull(text)[1] "🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED \"MY LIFE\" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣"# tweet with link, hastag, or @mentions
hashtag_id = 470421283638099968
data_train |> filter(id == hashtag_id) |> pull(text)[1] "Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk"# tweet where the text is just N/A
na_id <- 466302193877123072
data_train |> filter(id == na_id) |> pull(text)character(0)See what we get using just unnest_tokens(). So we are tokenizing here
tokens_tweet <- data_train |>
unnest_tokens(word, text, to_lower = TRUE, drop = FALSE) Here’s the tokenized post with the emoji:
tokens_tweet |>
filter(id == emoji_id) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| id | user_drinking | text | word |
|---|---|---|---|
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | j.clancy's |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | new |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | music |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | video |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | titled |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | my |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | life |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | releases |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | june |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | 1st |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | shot |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | amp |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | directed |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | by |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | phreshvision |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | http |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | t.co |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | zssxfrgnjq |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … http://t.co/zsSXfRGnjQ 6⃣1⃣ | 6⃣1⃣ |
Another common, but cumbersome, set of strings we see relate to links in tweets. These are marked with “http” and “t.co”. We see these in here and they likely aren’t useful for our modeling purpose.
Here’s the one with the “@” and “#” symbols. Note that these symbols (in word) are removed (compare to text). It used to be that unnest_tokens() preserved those. We will just use the raw text (without these special symbols) provided by unnest_tokens().
tokens_tweet |>
filter(id == hashtag_id) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| id | user_drinking | text | word |
|---|---|---|---|
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | can't |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wait |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | till |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | maxyice817 |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | legal |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | drinking |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | age |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | so |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | he |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | learns |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | pouring |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wine |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | amp |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | peanuts |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | in |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | an |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | adult |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | beverage |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | just |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | plain |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | stupid |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | growuppunk |
Tokenization strips “@” and “#” so we don’t have to do anything with those, but we will isolate links and replace the whole link with the string “sharedlink”. This will help us easily identify those after cleaning, if it is useful to do so.
Let’s make these adjustments.
data_train <- data_train |>
mutate(text = str_replace(text, "(https?://[^/\\s]+)[^\\s]*", "sharedlink")) |>
mutate(text = str_replace(text, "sharedlink.*", "sharedlink"))Let’s see how this affects our tokenization.
tokens <- data_train |>
unnest_tokens(word, text, to_lower = TRUE, drop = FALSE)
tokens |>
filter(id == hashtag_id) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| id | user_drinking | text | word |
|---|---|---|---|
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | can't |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wait |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | till |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | maxyice817 |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | legal |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | drinking |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | age |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | so |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | he |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | learns |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | pouring |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wine |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | amp |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | peanuts |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | in |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | an |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | adult |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | beverage |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | just |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | plain |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | stupid |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | growuppunk |
Let’s see how our cleaning has affected emojis.
tokens |>
filter(id == emoji_id) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| id | user_drinking | text | word |
|---|---|---|---|
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | j.clancy's |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | new |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | music |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | video |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | titled |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | my |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | life |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | releases |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | june |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | 1st |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | shot |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | amp |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | directed |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | by |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | phreshvision |
| 467354410902110208 | no | 🎥 J.CLANCY'S NEW MUSIC VIDEO TITLED "MY LIFE" RELEASES JUNE 1ST, SHOT & DIRECTED BY @PhreshVision … sharedlink | sharedlink |
Unfortunately, this process strips anything connected to emojis. Unfortunately, unnest_tokens() doesn’t support tokenizing emojis anymore, so we have to skip this. Let’s move on to looking at our cleaned tokens, both unigram and bigram.
# unigrams
tokens <- data_train |>
unnest_tokens(word, text, drop = FALSE)
# bigrams
tokens_bigram <- data_train |>
unnest_tokens(word, text, token = 'ngrams', n = 2, n_min = 1, drop = FALSE)
# Lets look at the bigrams this time, just for an example:
tokens_bigram |>
filter(id == hashtag_id) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| id | user_drinking | text | word |
|---|---|---|---|
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | can't |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | can't wait |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wait |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wait till |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | till |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | till maxyice817 |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | maxyice817 |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | maxyice817 is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is legal |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | legal |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | legal drinking |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | drinking |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | drinking age |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | age |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | age so |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | so |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | so he |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | he |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | he learns |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | learns |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | learns pouring |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | pouring |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | pouring wine |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wine |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | wine amp |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | amp |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | amp peanuts |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | peanuts |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | peanuts in |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | in |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | in an |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | an |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | an adult |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | adult |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | adult beverage |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | beverage |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | beverage is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | is just |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | just |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | just plain |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | plain |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | plain stupid |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | stupid |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | stupid growuppunk |
| 470421283638099968 | no | Can't wait till @maxyice817 is legal drinking age so he learns pouring wine & peanuts in an adult beverage is just plain stupid #GrowUpPunk | growuppunk |
Let’s decide what to do about N/A tweets.
data_train |>
filter(text == "N/A") |>
glimpse()Rows: 1,288
Columns: 3
$ id <dbl> 464583360711774208, 469317748267700224, 4626382884701716…
$ user_drinking <fct> no, no, no, no, no, no, no, no, no, no, no, no, no, no, …
$ text <chr> "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", "N/A", …This seems higher than the number of N/As we would expect simply from tweets that are only images/gifs. Doing some reading on twitter about tweet ids, turns out that tweet ids are actually unique URLs for the tweet - so we can more closely look at the content.
For many of these N/A tweets, it seems that the tweet has been deleted or made private since the publication of the data. Adding these identifiers to the twitter status URL gives us https://twitter.com/twitter/status/470353913011380224 which we see gives us a page that does not exist! Compare that to a non-N/A tweet https://twitter.com/twitter/status/468906415634251776
Let’s filter these N/A values out for now, but at the end we will consider a method of pulling in other data using tweet id to get date/time of the post.
Importantly, we will only filter N/A values out of the training portion of the splits since it would be biased to remove them from the validation data.
na_training_ids <- data_train |>
filter(text == "N/A") |>
pull(id)
data_train <- data_train |>
filter(!text == "N/A")Now that we know ids are supposed to be unique, lets see if there are any repeats.
any(duplicated(data_train$id))[1] TRUEdups <- data_train |>
filter(duplicated(data_train$id)) |>
pull(id)
data_train |>
filter(id %in% dups) |>
arrange(id) |>
View()Best to filter out duplicated tweets from full data set. Here’s how you can do that.
data_train <- data_train |>
filter(!duplicated(data_train$id))Explore tokens
Let’s look at our tokens a little more. First, we look at the total and unique numbers of bigrams and unigrams.
# make tokens
tokens <- data_train |>
unnest_tokens(word, text, drop = FALSE)
tokens_bigram <- data_train |>
unnest_tokens(word, text, token = 'ngrams', n = 2, n_min = 1, drop = FALSE)There are length(tokens$word) unigram tokens and of those length(unique(tokens$word)) are unique (sometimes called “types” as opposed to “tokens”). Conversely, there are length(tokens_bigram$word) bigrams, length(unique(tokens_bigram$word)) of which are unique.
The distribution of the top 1k tokens (across both unigrams and bigrams) is shown below. As is usually the case, the distribution is highly skewed, with a few tokens representing the majority, and many appearing increasingly less in the long tail.
# Unigrams
tokens |>
count(word, sort = TRUE) |>
slice(1:1000) |>
mutate(word = reorder(word, -n)) |>
ggplot(aes(word, n)) +
geom_col() +
xlab('Words') +
ylab('Raw Count') +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank())
Skimming the top terms confirms that we did enough cleaning. We see again the effects of filtering this data set – a lot of our top terms are drinking related.
# Unigrams
tokens |>
count(word, sort = TRUE) |>
slice(1:20) |>
mutate(word = reorder(word, -n)) |>
ggplot(aes(word, n)) +
geom_col(color = "black") +
xlab('Words') +
ylab('Raw Count') +
theme(axis.text.x=element_text(angle = 60, hjust = 1))
Here’s a scrollable table to look through all the top 1000.
tokens |>
count(word, sort = TRUE) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| word | n |
|---|---|
| the | 825 |
| a | 805 |
| i | 742 |
| to | 608 |
| and | 557 |
| my | 533 |
| sharedlink | 521 |
| of | 367 |
| up | 366 |
| party | 356 |
| in | 313 |
| at | 307 |
| drunk | 307 |
| is | 290 |
| you | 290 |
| i'm | 270 |
| beer | 247 |
| me | 242 |
| on | 241 |
| for | 236 |
| shot | 232 |
| with | 205 |
| that | 204 |
| bar | 193 |
| this | 186 |
| just | 185 |
| it | 179 |
| club | 173 |
| fucked | 173 |
| get | 167 |
| amp | 155 |
| so | 152 |
| have | 145 |
| wine | 139 |
| was | 138 |
| be | 135 |
| by | 129 |
| when | 127 |
| like | 126 |
| drinking | 118 |
| all | 110 |
| not | 101 |
| turn | 94 |
| out | 93 |
| but | 92 |
| now | 92 |
| bottle | 89 |
| it's | 89 |
| are | 86 |
| if | 86 |
| got | 85 |
| time | 82 |
| we | 81 |
| night | 80 |
| ny | 80 |
| one | 79 |
| can | 78 |
| good | 78 |
| tonight | 77 |
| your | 76 |
| lol | 69 |
| do | 68 |
| don't | 66 |
| life | 66 |
| or | 66 |
| some | 66 |
| day | 64 |
| go | 64 |
| no | 63 |
| what | 62 |
| need | 60 |
| alcohol | 59 |
| getting | 59 |
| rochester | 59 |
| love | 58 |
| they | 58 |
| wasted | 57 |
| from | 56 |
| how | 56 |
| pub | 56 |
| really | 54 |
| too | 54 |
| an | 53 |
| drink | 53 |
| who | 53 |
| new | 52 |
| had | 51 |
| people | 51 |
| going | 50 |
| want | 50 |
| about | 49 |
| shit | 49 |
| will | 49 |
| last | 47 |
| right | 47 |
| know | 46 |
| as | 45 |
| he | 45 |
| come | 44 |
| today | 44 |
| 2 | 43 |
| can't | 43 |
| great | 42 |
| over | 42 |
| she | 42 |
| then | 40 |
| w | 40 |
| after | 38 |
| has | 38 |
| house | 38 |
| off | 38 |
| u | 38 |
| work | 38 |
| fuck | 37 |
| much | 37 |
| back | 36 |
| bad | 36 |
| her | 36 |
| still | 36 |
| think | 36 |
| girl | 35 |
| music | 35 |
| see | 35 |
| i'll | 34 |
| wanna | 34 |
| best | 33 |
| take | 33 |
| 3 | 32 |
| because | 32 |
| here | 32 |
| liquor | 32 |
| only | 32 |
| water | 32 |
| b | 31 |
| better | 31 |
| country | 31 |
| gonna | 31 |
| i've | 31 |
| make | 31 |
| why | 31 |
| would | 31 |
| someone | 30 |
| you're | 30 |
| am | 29 |
| been | 29 |
| that's | 29 |
| whiskey | 29 |
| even | 28 |
| home | 28 |
| more | 28 |
| our | 28 |
| feel | 27 |
| 1st | 26 |
| bottles | 26 |
| brew | 26 |
| ever | 26 |
| let's | 26 |
| birthday | 25 |
| dinosaur | 25 |
| happy | 25 |
| june | 25 |
| lmao | 25 |
| should | 25 |
| there | 25 |
| video | 25 |
| way | 25 |
| while | 25 |
| fucking | 24 |
| im | 24 |
| into | 24 |
| syracuse | 24 |
| tequila | 24 |
| ass | 23 |
| down | 23 |
| every | 23 |
| his | 23 |
| j.clancy's | 23 |
| mom | 23 |
| oh | 23 |
| phreshvision | 23 |
| que | 23 |
| releases | 23 |
| titled | 23 |
| girls | 22 |
| haha | 22 |
| its | 22 |
| man | 22 |
| real | 22 |
| summer | 22 |
| than | 22 |
| these | 22 |
| tomorrow | 22 |
| turnup | 22 |
| vodka | 22 |
| weekend | 22 |
| world | 22 |
| 1 | 21 |
| always | 21 |
| bitch | 21 |
| dad | 21 |
| directed | 21 |
| first | 21 |
| food | 21 |
| golf | 21 |
| guys | 21 |
| them | 21 |
| us | 21 |
| 4 | 20 |
| ale | 20 |
| big | 20 |
| could | 20 |
| damn | 20 |
| everyone | 20 |
| game | 20 |
| high | 20 |
| lean | 20 |
| light | 20 |
| made | 20 |
| others | 20 |
| said | 20 |
| watching | 20 |
| anyone | 19 |
| before | 19 |
| family | 19 |
| friend | 19 |
| guy | 19 |
| him | 19 |
| never | 19 |
| wait | 19 |
| well | 19 |
| were | 19 |
| where | 19 |
| year | 19 |
| yo | 19 |
| again | 18 |
| binge | 18 |
| dinner | 18 |
| does | 18 |
| friends | 18 |
| grill | 18 |
| gt | 18 |
| hangover | 18 |
| having | 18 |
| morning | 18 |
| next | 18 |
| old | 18 |
| put | 18 |
| two | 18 |
| hit | 17 |
| hope | 17 |
| join | 17 |
| little | 17 |
| pretty | 17 |
| since | 17 |
| trying | 17 |
| ya | 17 |
| being | 16 |
| came | 16 |
| days | 16 |
| end | 16 |
| gets | 16 |
| glass | 16 |
| ipa | 16 |
| mad | 16 |
| money | 16 |
| remember | 16 |
| run | 16 |
| there's | 16 |
| thing | 16 |
| whole | 16 |
| 30 | 15 |
| 5 | 15 |
| cause | 15 |
| coming | 15 |
| crown | 15 |
| eat | 15 |
| fun | 15 |
| hate | 15 |
| help | 15 |
| long | 15 |
| lot | 15 |
| myself | 15 |
| nice | 15 |
| saturday | 15 |
| school | 15 |
| show | 15 |
| sleep | 15 |
| something | 15 |
| thanks | 15 |
| turnt | 15 |
| week | 15 |
| went | 15 |
| yes | 15 |
| 7 | 14 |
| any | 14 |
| bed | 14 |
| bout | 14 |
| did | 14 |
| didn't | 14 |
| done | 14 |
| gotta | 14 |
| irish | 14 |
| left | 14 |
| look | 14 |
| n | 14 |
| phone | 14 |
| pizza | 14 |
| playing | 14 |
| say | 14 |
| screen | 14 |
| tour | 14 |
| yet | 14 |
| actually | 13 |
| athletic | 13 |
| baby | 13 |
| bitches | 13 |
| breakfast | 13 |
| bud | 13 |
| car | 13 |
| champagne | 13 |
| crazy | 13 |
| dance | 13 |
| face | 13 |
| ice | 13 |
| isn't | 13 |
| later | 13 |
| lets | 13 |
| lt | 13 |
| most | 13 |
| needs | 13 |
| nothing | 13 |
| probably | 13 |
| room | 13 |
| rt | 13 |
| told | 13 |
| wish | 13 |
| wrong | 13 |
| yak | 13 |
| alcoholic | 12 |
| around | 12 |
| booze | 12 |
| cold | 12 |
| drinks | 12 |
| drive | 12 |
| friday | 12 |
| god | 12 |
| hard | 12 |
| let | 12 |
| literally | 12 |
| might | 12 |
| open | 12 |
| own | 12 |
| perfect | 12 |
| red | 12 |
| roc | 12 |
| talk | 12 |
| talking | 12 |
| times | 12 |
| welcome | 12 |
| 12 | 11 |
| bbq | 11 |
| boys | 11 |
| buy | 11 |
| dinobbq | 11 |
| else | 11 |
| everything | 11 |
| free | 11 |
| give | 11 |
| grad | 11 |
| guess | 11 |
| keep | 11 |
| making | 11 |
| other | 11 |
| park | 11 |
| place | 11 |
| play | 11 |
| pool | 11 |
| rum | 11 |
| sitting | 11 |
| store | 11 |
| such | 11 |
| super | 11 |
| their | 11 |
| tho | 11 |
| though | 11 |
| tipsy | 11 |
| wants | 11 |
| 10 | 10 |
| 6 | 10 |
| 8 | 10 |
| alone | 10 |
| another | 10 |
| bc | 10 |
| book | 10 |
| brewing | 10 |
| chocolate | 10 |
| college | 10 |
| company | 10 |
| cool | 10 |
| de | 10 |
| dont | 10 |
| front | 10 |
| graduation | 10 |
| hammered | 10 |
| holy | 10 |
| live | 10 |
| moment | 10 |
| niggas | 10 |
| ok | 10 |
| ready | 10 |
| restaurant | 10 |
| root | 10 |
| start | 10 |
| strip | 10 |
| tasting | 10 |
| text | 10 |
| thank | 10 |
| things | 10 |
| thought | 10 |
| took | 10 |
| wasn't | 10 |
| we're | 10 |
| wegmans | 10 |
| which | 10 |
| years | 10 |
| yesterday | 10 |
| ask | 9 |
| cinco | 9 |
| ciroc | 9 |
| city | 9 |
| cream | 9 |
| driving | 9 |
| eye | 9 |
| finally | 9 |
| hell | 9 |
| hour | 9 |
| id | 9 |
| instead | 9 |
| ithaca | 9 |
| lager | 9 |
| many | 9 |
| may | 9 |
| mind | 9 |
| miss | 9 |
| parents | 9 |
| person | 9 |
| prom | 9 |
| same | 9 |
| seriously | 9 |
| sit | 9 |
| sorry | 9 |
| sports | 9 |
| stop | 9 |
| stupid | 9 |
| tap | 9 |
| taste | 9 |
| tea | 9 |
| tell | 9 |
| through | 9 |
| throw | 9 |
| ur | 9 |
| wonder | 9 |
| working | 9 |
| wtf | 9 |
| yeah | 9 |
| 2014 | 8 |
| 9 | 8 |
| af | 8 |
| ain't | 8 |
| almost | 8 |
| anything | 8 |
| beautiful | 8 |
| beers | 8 |
| black | 8 |
| chicken | 8 |
| doesn't | 8 |
| eating | 8 |
| favorite | 8 |
| finals | 8 |
| find | 8 |
| genesee | 8 |
| hahaha | 8 |
| hair | 8 |
| hand | 8 |
| handle | 8 |
| head | 8 |
| horse | 8 |
| job | 8 |
| kind | 8 |
| late | 8 |
| listening | 8 |
| makes | 8 |
| mayo | 8 |
| mean | 8 |
| movie | 8 |
| okay | 8 |
| point | 8 |
| season | 8 |
| started | 8 |
| sunday | 8 |
| taking | 8 |
| thats | 8 |
| together | 8 |
| trashed | 8 |
| very | 8 |
| watch | 8 |
| what's | 8 |
| women | 8 |
| won't | 8 |
| worst | 8 |
| already | 7 |
| ball | 7 |
| bday | 7 |
| biggest | 7 |
| body | 7 |
| bring | 7 |
| bus | 7 |
| call | 7 |
| care | 7 |
| cat | 7 |
| celebrating | 7 |
| cheese | 7 |
| corning | 7 |
| doing | 7 |
| drank | 7 |
| dream | 7 |
| dudes | 7 |
| early | 7 |
| enjoying | 7 |
| enough | 7 |
| evening | 7 |
| everybody | 7 |
| excited | 7 |
| fact | 7 |
| finish | 7 |
| fuckin | 7 |
| full | 7 |
| goes | 7 |
| henny | 7 |
| idea | 7 |
| ima | 7 |
| jump | 7 |
| kill | 7 |
| lake | 7 |
| lil | 7 |
| maybe | 7 |
| means | 7 |
| men | 7 |
| minutes | 7 |
| name | 7 |
| ocallaghanpub | 7 |
| outside | 7 |
| peach | 7 |
| picking | 7 |
| r | 7 |
| rn | 7 |
| says | 7 |
| scotch | 7 |
| senior | 7 |
| sex | 7 |
| she's | 7 |
| shots | 7 |
| single | 7 |
| sip | 7 |
| sister | 7 |
| sounds | 7 |
| sun | 7 |
| sure | 7 |
| thinking | 7 |
| those | 7 |
| toast | 7 |
| walk | 7 |
| wedding | 7 |
| weed | 7 |
| weeks | 7 |
| without | 7 |
| woke | 7 |
| york | 7 |
| yum | 7 |
| zoo | 7 |
| 100 | 6 |
| 20 | 6 |
| 21 | 6 |
| acme | 6 |
| ago | 6 |
| away | 6 |
| beat | 6 |
| blast | 6 |
| bought | 6 |
| boy | 6 |
| check | 6 |
| chill | 6 |
| cocktail | 6 |
| coffee | 6 |
| couldn't | 6 |
| crawl | 6 |
| crying | 6 |
| cup | 6 |
| date | 6 |
| dogs | 6 |
| downtown | 6 |
| drumlins | 6 |
| enjoy | 6 |
| feeling | 6 |
| feels | 6 |
| few | 6 |
| fight | 6 |
| final | 6 |
| fire | 6 |
| forget | 6 |
| funny | 6 |
| garden | 6 |
| geneseo | 6 |
| half | 6 |
| he's | 6 |
| hear | 6 |
| hold | 6 |
| hop | 6 |
| hot | 6 |
| hours | 6 |
| invited | 6 |
| joe | 6 |
| juice | 6 |
| kick | 6 |
| kids | 6 |
| kiss | 6 |
| lady | 6 |
| lonely | 6 |
| looking | 6 |
| lounge | 6 |
| met | 6 |
| nah | 6 |
| nigga | 6 |
| o | 6 |
| o'callaghan's | 6 |
| omg | 6 |
| once | 6 |
| past | 6 |
| pictures | 6 |
| question | 6 |
| rit | 6 |
| rock | 6 |
| seeing | 6 |
| seen | 6 |
| semester | 6 |
| small | 6 |
| smh | 6 |
| smoke | 6 |
| soon | 6 |
| stay | 6 |
| street | 6 |
| tattoo | 6 |
| tonight's | 6 |
| true | 6 |
| trust | 6 |
| 6 | |
| understand | 6 |
| until | 6 |
| use | 6 |
| used | 6 |
| wake | 6 |
| wanted | 6 |
| who's | 6 |
| wow | 6 |
| 0 | 5 |
| 11 | 5 |
| 15 | 5 |
| 24 | 5 |
| _bliss24 | 5 |
| afternoon | 5 |
| america | 5 |
| authentic | 5 |
| band | 5 |
| bartender | 5 |
| behind | 5 |
| beverage | 5 |
| bored | 5 |
| broke | 5 |
| brother | 5 |
| california | 5 |
| calling | 5 |
| cheers | 5 |
| class | 5 |
| co | 5 |
| comes | 5 |
| congrats | 5 |
| couch | 5 |
| couple | 5 |
| currently | 5 |
| d | 5 |
| death | 5 |
| dinosaurbq | 5 |
| doe | 5 |
| dress | 5 |
| dude | 5 |
| dumb | 5 |
| eggs | 5 |
| energy | 5 |
| falls | 5 |
| finger | 5 |
| fit | 5 |
| floor | 5 |
| geneseebrewery | 5 |
| georgia | 5 |
| gettin | 5 |
| greatest | 5 |
| happen | 5 |
| hey | 5 |
| ill | 5 |
| inn | 5 |
| jack | 5 |
| johnny's | 5 |
| k | 5 |
| kid | 5 |
| land | 5 |
| leave | 5 |
| listen | 5 |
| lost | 5 |
| lots | 5 |
| lunch | 5 |
| mazz | 5 |
| meet | 5 |
| memories | 5 |
| monday | 5 |
| move | 5 |
| nobody | 5 |
| pale | 5 |
| part | 5 |
| pick | 5 |
| played | 5 |
| pop | 5 |
| porch | 5 |
| pour | 5 |
| pussy | 5 |
| quick | 5 |
| rangers | 5 |
| rather | 5 |
| s | 5 |
| samueladamsbeer | 5 |
| saw | 5 |
| schedule | 5 |
| second | 5 |
| seconds | 5 |
| session | 5 |
| set | 5 |
| shopping | 5 |
| silver | 5 |
| smells | 5 |
| song | 5 |
| spend | 5 |
| stand | 5 |
| straight | 5 |
| swear | 5 |
| syracuseu | 5 |
| tf | 5 |
| till | 5 |
| top | 5 |
| town | 5 |
| triple | 5 |
| type | 5 |
| waiting | 5 |
| weird | 5 |
| white | 5 |
| win | 5 |
| wit | 5 |
| word | 5 |
| would've | 5 |
| wouldn't | 5 |
| y'all | 5 |
| yours | 5 |
| 22 | 4 |
| 2nd | 4 |
| action | 4 |
| agree | 4 |
| al | 4 |
| allowed | 4 |
| alright | 4 |
| also | 4 |
| american | 4 |
| anyway | 4 |
| aren't | 4 |
| asking | 4 |
| asleep | 4 |
| available | 4 |
| awesome | 4 |
| awkward | 4 |
| aye | 4 |
| backyard | 4 |
| bacon | 4 |
| bag | 4 |
| baja | 4 |
| beach | 4 |
| belly | 4 |
| beyond | 4 |
| bistro | 4 |
| bit | 4 |
| blind | 4 |
| block | 4 |
| blood | 4 |
| box | 4 |
| boyfriend | 4 |
| brothers | 4 |
| brought | 4 |
| buddy's | 4 |
| bull | 4 |
| bunch | 4 |
| cabinet | 4 |
| called | 4 |
| canandaigua | 4 |
| cant | 4 |
| cap | 4 |
| cbcraftbrewers | 4 |
| celebrate | 4 |
| clean | 4 |
| coleman's | 4 |
| cornell | 4 |
| creek | 4 |
| cry | 4 |
| cuz | 4 |
| dairy | 4 |
| dave | 4 |
| dead | 4 |
| deal | 4 |
| dear | 4 |
| deck | 4 |
| definitely | 4 |
| door | 4 |
| draft | 4 |
| dropped | 4 |
| eddies | 4 |
| either | 4 |
| empire | 4 |
| ended | 4 |
| ending | 4 |
| entire | 4 |
| eyes | 4 |
| faced | 4 |
| faded | 4 |
| fall | 4 |
| fan | 4 |
| fans | 4 |
| fast | 4 |
| fav | 4 |
| fine | 4 |
| fixed | 4 |
| flights | 4 |
| fly | 4 |
| follow | 4 |
| football | 4 |
| forgot | 4 |
| forward | 4 |
| gates | 4 |
| gave | 4 |
| genny | 4 |
| gone | 4 |
| grab | 4 |
| grinding | 4 |
| hang | 4 |
| happened | 4 |
| hates | 4 |
| heart | 4 |
| hill | 4 |
| homies | 4 |
| honestly | 4 |
| honey | 4 |
| hub | 4 |
| i'd | 4 |
| joke | 4 |
| judge | 4 |
| kinda | 4 |
| knew | 4 |
| knows | 4 |
| legit | 4 |
| line | 4 |
| lmfao | 4 |
| lose | 4 |
| loved | 4 |
| loyal | 4 |
| middle | 4 |
| midtown | 4 |
| midtown_roc | 4 |
| milk | 4 |
| missing | 4 |
| mixed | 4 |
| mr | 4 |
| naishekirah | 4 |
| needa | 4 |
| nhl | 4 |
| nightclub | 4 |
| nights | 4 |
| normal | 4 |
| pass | 4 |
| pay | 4 |
| period | 4 |
| personal | 4 |
| photo | 4 |
| plan | 4 |
| plastered | 4 |
| please | 4 |
| pong | 4 |
| posted | 4 |
| pouring | 4 |
| problem | 4 |
| pull | 4 |
| putting | 4 |
| random | 4 |
| republican | 4 |
| river | 4 |
| rose | 4 |
| round | 4 |
| route | 4 |
| running | 4 |
| saison | 4 |
| saying | 4 |
| seneca | 4 |
| shower | 4 |
| sick | 4 |
| side | 4 |
| sipping | 4 |
| slumber | 4 |
| soccer | 4 |
| soda | 4 |
| solid | 4 |
| sorts | 4 |
| special | 4 |
| stafford | 4 |
| stage | 4 |
| star | 4 |
| starting | 4 |
| style | 4 |
| sucks | 4 |
| sunshine | 4 |
| sushi | 4 |
| tastes | 4 |
| tavern | 4 |
| terrible | 4 |
| test | 4 |
| texted | 4 |
| theatre | 4 |
| themselves | 4 |
| thirsty | 4 |
| thru | 4 |
| touch | 4 |
| trip | 4 |
| turkey | 4 |
| tweet | 4 |
| ugh | 4 |
| under | 4 |
| v | 4 |
| visit | 4 |
| vote | 4 |
| walked | 4 |
| whoever | 4 |
| williammn34 | 4 |
| wings | 4 |
| yik | 4 |
| you've | 4 |
| young | 4 |
| zach | 4 |
| 13 | 3 |
| 14 | 3 |
| 31 | 3 |
| 390 | 3 |
| 40 | 3 |
| 50 | 3 |
| 78 | 3 |
| able | 3 |
| absolutely | 3 |
| admit | 3 |
| adult | 3 |
| against | 3 |
| aka | 3 |
| amandaleigh007 | 3 |
| amount | 3 |
| anheuserbusch | 3 |
| arms | 3 |
| asf | 3 |
| asked | 3 |
| attitude | 3 |
| ayee | 3 |
| bakershorty1022 | 3 |
| banger | 3 |
| bathroom | 3 |
| become | 3 |
| bee | 3 |
| belgian | 3 |
| believe | 3 |
| bellevue | 3 |
| bellsbrewery | 3 |
| bianca_pitonzo | 3 |
| bill | 3 |
| blame | 3 |
| blew | 3 |
| blonde | 3 |
| blu | 3 |
| blue | 3 |
| blunts | 3 |
| both | 3 |
| bouta | 3 |
| bowl | 3 |
| bro | 3 |
| brockport | 3 |
| budget | 3 |
| build | 3 |
| c | 3 |
| cafe | 3 |
| calls | 3 |
| cans | 3 |
| case | 3 |
| cellars | 3 |
| change | 3 |
| chili | 3 |
| chillin | 3 |
| chilling | 3 |
| choice | 3 |
| cincodemayo | 3 |
| clock | 3 |
| close | 3 |
| clothes | 3 |
| combo | 3 |
| computer | 3 |
| condescendtorts | 3 |
| content | 3 |
| coors | 3 |
| course | 3 |
| crap | 3 |
| crash | 3 |
| creep | 3 |
| cut | 3 |
| cute | 3 |
| daily | 3 |
| dancing | 3 |
| daughter | 3 |
| delicious | 3 |
| dew | 3 |
| diceeedagod | 3 |
| discount | 3 |
| dope | 3 |
| double | 3 |
| dragon | 3 |
| drinkin | 3 |
| drugs | 3 |
| dunno | 3 |
| ed | 3 |
| eddie | 3 |
| effort | 3 |
| excellent | 3 |
| except | 3 |
| experience | 3 |
| extremely | 3 |
| f | 3 |
| fam | 3 |
| far | 3 |
| fb | 3 |
| feelin | 3 |
| felt | 3 |
| festival | 3 |
| fighting | 3 |
| fill | 3 |
| finished | 3 |
| fish | 3 |
| fitness | 3 |
| fix | 3 |
| flip | 3 |
| foh | 3 |
| foodporn | 3 |
| form | 3 |
| found | 3 |
| four | 3 |
| freedom | 3 |
| freshman | 3 |
| fridge | 3 |
| frozen | 3 |
| fruit | 3 |
| gas | 3 |
| gatorade | 3 |
| gay | 3 |
| generation | 3 |
| glad | 3 |
| glasses | 3 |
| godzilla | 3 |
| goin | 3 |
| grades | 3 |
| grandma | 3 |
| green | 3 |
| group | 3 |
| gun | 3 |
| gunna | 3 |
| hahahaha | 3 |
| hall | 3 |
| hanging | 3 |
| happens | 3 |
| hat | 3 |
| haven't | 3 |
| hayverk | 3 |
| heads | 3 |
| higher | 3 |
| hip | 3 |
| hockey | 3 |
| holes | 3 |
| hungover | 3 |
| idk | 3 |
| imma | 3 |
| interview | 3 |
| ithacacollege | 3 |
| jake | 3 |
| joint | 3 |
| jon | 3 |
| jordan | 3 |
| july | 3 |
| jyolevich | 3 |
| k_m_wx2 | 3 |
| karaoke | 3 |
| kicked | 3 |
| king | 3 |
| klondike | 3 |
| knee | 3 |
| knees | 3 |
| kyle | 3 |
| kölsch | 3 |
| lakes | 3 |
| lame | 3 |
| lastnight | 3 |
| league | 3 |
| learn | 3 |
| leaving | 3 |
| library | 3 |
| lights | 3 |
| lime | 3 |
| list | 3 |
| lit | 3 |
| lmaoo | 3 |
| local | 3 |
| lodge | 3 |
| longer | 3 |
| looks | 3 |
| m | 3 |
| main | 3 |
| mall | 3 |
| margarita | 3 |
| market | 3 |
| maxyice817 | 3 |
| meant | 3 |
| memorial | 3 |
| memory | 3 |
| merica | 3 |
| mfs | 3 |
| michael | 3 |
| mint | 3 |
| mirage | 3 |
| missed | 3 |
| mmm | 3 |
| momma | 3 |
| moscato | 3 |
| mouth | 3 |
| movies | 3 |
| mtn_dew | 3 |
| mug | 3 |
| nap | 3 |
| nasty | 3 |
| needed | 3 |
| netflix | 3 |
| nofilter | 3 |
| noise | 3 |
| none | 3 |
| noon | 3 |
| number | 3 |
| ontario | 3 |
| onto | 3 |
| opening | 3 |
| otherwise | 3 |
| outta | 3 |
| owe | 3 |
| parties | 3 |
| partying | 3 |
| partyprobs | 3 |
| passed | 3 |
| patio | 3 |
| petewoytowich | 3 |
| pics | 3 |
| pint | 3 |
| pissed | 3 |
| pittsford | 3 |
| plane | 3 |
| planning | 3 |
| pm | 3 |
| pooper | 3 |
| popped | 3 |
| ppl | 3 |
| pregnant | 3 |
| proof | 3 |
| quarter | 3 |
| questions | 3 |
| quite | 3 |
| racquet | 3 |
| radio | 3 |
| re | 3 |
| read | 3 |
| realize | 3 |
| realized | 3 |
| realizing | 3 |
| rebel | 3 |
| reese's | 3 |
| relationship | 3 |
| retweet | 3 |
| ride | 3 |
| riding | 3 |
| ring | 3 |
| road | 3 |
| roll | 3 |
| safe | 3 |
| sam's | 3 |
| sat | 3 |
| save | 3 |
| search | 3 |
| send | 3 |
| series | 3 |
| shitty | 3 |
| shoes | 3 |
| sized | 3 |
| sleeping | 3 |
| smell | 3 |
| smile | 3 |
| snacks | 3 |
| sober | 3 |
| social | 3 |
| somebody | 3 |
| somewhere | 3 |
| son | 3 |
| songs | 3 |
| soooo | 3 |
| sooooo | 3 |
| spending | 3 |
| spent | 3 |
| spilled | 3 |
| splitrock | 3 |
| spray | 3 |
| squirt | 3 |
| standing | 3 |
| stars | 3 |
| starts | 3 |
| steal | 3 |
| stories | 3 |
| strawberry | 3 |
| stuff | 3 |
| sum | 3 |
| summerofbaja | 3 |
| supposed | 3 |
| swag | 3 |
| swimming | 3 |
| taken | 3 |
| team | 3 |
| teeth | 3 |
| tells | 3 |
| texts | 3 |
| thelavaclub | 3 |
| thesak9599 | 3 |
| they're | 3 |
| thinkin | 3 |
| thou | 3 |
| thoughts | 3 |
| three | 3 |
| thursday | 3 |
| tiger | 3 |
| til | 3 |
| toby | 3 |
| tournament | 3 |
| truly | 3 |
| try | 3 |
| tryna | 3 |
| tuesday | 3 |
| turning | 3 |
| tweets | 3 |
| ugly | 3 |
| using | 3 |
| vh1top20 | 3 |
| walks | 3 |
| wall | 3 |
| wednesday | 3 |
| whatever | 3 |
| where's | 3 |
| whisky | 3 |
| whose | 3 |
| wife | 3 |
| won | 3 |
| works | 3 |
| worse | 3 |
| worth | 3 |
| x | 3 |
| xbox | 3 |
| xcorinneroberts | 3 |
| y | 3 |
| yoo | 3 |
| you'll | 3 |
| youre | 3 |
| yr | 3 |
| yup | 3 |
| 13wham | 2 |
| 140 | 2 |
| 16 | 2 |
| 17 | 2 |
| 18 | 2 |
| 2015lmcpredictions | 2 |
| 20th | 2 |
| 21st | 2 |
| 23 | 2 |
| 25 | 2 |
| 308 | 2 |
| 3rd | 2 |
| 46 | 2 |
| 5th | 2 |
| 60 | 2 |
| 6s | 2 |
| 90 | 2 |
| 9th | 2 |
| _papiiiichulo_ | 2 |
| a1 | 2 |
| aatcdreleaseparty | 2 |
| abandon | 2 |
| abc | 2 |
| abilene | 2 |
| abuse | 2 |
| acknowledge | 2 |
| across | 2 |
| act | 2 |
| acting | 2 |
| adams | 2 |
| add | 2 |
| advantage | 2 |
| age | 2 |
| aged | 2 |
| ahead | 2 |
| airport | 2 |
| alex | 2 |
| alive | 2 |
| allergies | 2 |
| allyssaquinones | 2 |
| amazing | 2 |
| amber | 2 |
| amore | 2 |
| anaaamadden | 2 |
| angry | 2 |
| anna | 2 |
| anniversary | 2 |
| annual | 2 |
| anymore | 2 |
| anytime | 2 |
| apartment | 2 |
| apologies | 2 |
| app | 2 |
| apple | 2 |
| appropriate | 2 |
| arcadia | 2 |
| archives | 2 |
| area | 2 |
| arena | 2 |
| art | 2 |
| aspen | 2 |
| ate | 2 |
| athena | 2 |
| attention | 2 |
| attire | 2 |
| aunts | 2 |
| avg | 2 |
| avon | 2 |
| aww | 2 |
| babe | 2 |
| babes | 2 |
| babynep | 2 |
| bachelor | 2 |
| backward | 2 |
| badly | 2 |
| bae | 2 |
| barbie | 2 |
| baseball | 2 |
| basically | 2 |
| batavia | 2 |
| bayside | 2 |
| bear | 2 |
| beauty | 2 |
| beehive | 2 |
| below | 2 |
| berliner | 2 |
| bestfriend | 2 |
| bet | 2 |
| between | 2 |
| bf | 2 |
| biancas | 2 |
| birdland | 2 |
| bière | 2 |
| blaring | 2 |
| bleeding | 2 |
| blender | 2 |
| blow | 2 |
| blueberry | 2 |
| bluwolfbistro | 2 |
| boi | 2 |
| books | 2 |
| boom | 2 |
| boss | 2 |
| bounce | 2 |
| boutta | 2 |
| bowling | 2 |
| bradsweet52 | 2 |
| braid | 2 |
| bread | 2 |
| break | 2 |
| breaking | 2 |
| brewery | 2 |
| breweryommegang | 2 |
| brianna | 2 |
| bridget | 2 |
| broken | 2 |
| bros | 2 |
| bruh | 2 |
| buckets | 2 |
| buddys | 2 |
| buffet | 2 |
| burgers | 2 |
| business | 2 |
| bust | 2 |
| buying | 2 |
| buys | 2 |
| cake | 2 |
| cannot | 2 |
| capture | 2 |
| careful | 2 |
| carrissa | 2 |
| carry | 2 |
| caught | 2 |
| celebration | 2 |
| center | 2 |
| chair | 2 |
| champion | 2 |
| changed | 2 |
| charity | 2 |
| chase | 2 |
| chatting | 2 |
| cheap | 2 |
| cheeks | 2 |
| choices | 2 |
| choose | 2 |
| chris | 2 |
| chrome | 2 |
| classic | 2 |
| classmates | 2 |
| classy | 2 |
| cleaned | 2 |
| clear | 2 |
| clearly | 2 |
| cliff | 2 |
| clutch | 2 |
| cmac | 2 |
| cocky | 2 |
| coconut | 2 |
| collegefession | 2 |
| comedy | 2 |
| commenced | 2 |
| competition | 2 |
| continue | 2 |
| cookies | 2 |
| cop | 2 |
| cops | 2 |
| corona | 2 |
| cos | 2 |
| cousins | 2 |
| cracked | 2 |
| crans | 2 |
| cray | 2 |
| crew | 2 |
| crosby | 2 |
| crossfit | 2 |
| crotty | 2 |
| crush | 2 |
| cuisine | 2 |
| cuse | 2 |
| da | 2 |
| dad's | 2 |
| daniel's | 2 |
| dark | 2 |
| dat | 2 |
| dd | 2 |
| deadass | 2 |
| debating | 2 |
| debris | 2 |
| decent | 2 |
| dedicated | 2 |
| deep | 2 |
| definite | 2 |
| delaware | 2 |
| denied | 2 |
| depewsgentleman | 2 |
| depo | 2 |
| depressed | 2 |
| deserve | 2 |
| devkelly2 | 2 |
| dick | 2 |
| didnt | 2 |
| die | 2 |
| diet | 2 |
| different | 2 |
| dining | 2 |
| dirty | 2 |
| disappear | 2 |
| disappointed | 2 |
| discovered | 2 |
| disney | 2 |
| dj | 2 |
| doesnt | 2 |
| don | 2 |
| dos | 2 |
| dove | 2 |
| dr | 2 |
| draught | 2 |
| dreams | 2 |
| dressed | 2 |
| dressing | 2 |
| drinker | 2 |
| drop | 2 |
| due | 2 |
| dumped | 2 |
| duran | 2 |
| durant | 2 |
| during | 2 |
| durk | 2 |
| dustin | 2 |
| dverk15 | 2 |
| e | 2 |
| each | 2 |
| ear | 2 |
| east | 2 |
| easy | 2 |
| edge | 2 |
| effects | 2 |
| elbow | 2 |
| electronic | 2 |
| elysianbrewing | 2 |
| emporium | 2 |
| equis | 2 |
| eric | 2 |
| erinconnorton | 2 |
| especially | 2 |
| event | 2 |
| everyday | 2 |
| everytime | 2 |
| evryoneh8schris | 2 |
| excitement | 2 |
| exercise | 2 |
| exit33_ts | 2 |
| extra | 2 |
| eyebrows | 2 |
| fab | 2 |
| facts | 2 |
| fancy | 2 |
| feelings | 2 |
| fell | 2 |
| fiber | 2 |
| field | 2 |
| finale | 2 |
| finding | 2 |
| fires | 2 |
| fitting | 2 |
| flat | 2 |
| flavor | 2 |
| float | 2 |
| florida | 2 |
| flower | 2 |
| flowers | 2 |
| folks | 2 |
| formal | 2 |
| fortune | 2 |
| fountain | 2 |
| frat | 2 |
| friendly | 2 |
| fries | 2 |
| fucks | 2 |
| function | 2 |
| fund | 2 |
| funday | 2 |
| funniest | 2 |
| gdoublet | 2 |
| gerardyamber | 2 |
| gin | 2 |
| ginger | 2 |
| glassfest | 2 |
| glenora | 2 |
| glory | 2 |
| glow | 2 |
| goal | 2 |
| goals | 2 |
| gobowlingdotcom | 2 |
| gold | 2 |
| gon | 2 |
| goodnight | 2 |
| gooseisland | 2 |
| gordon | 2 |
| gossip | 2 |
| government | 2 |
| greece | 2 |
| greenflashbeer | 2 |
| grille | 2 |
| grilling | 2 |
| grown | 2 |
| gusta | 2 |
| gym | 2 |
| hahahahah | 2 |
| happening | 2 |
| happyhour | 2 |
| happymothersday | 2 |
| harbor | 2 |
| hardcore | 2 |
| harvest | 2 |
| hasn't | 2 |
| haus | 2 |
| heard | 2 |
| hearts | 2 |
| heaven | 2 |
| hes | 2 |
| hilarious | 2 |
| hills | 2 |
| hiphop | 2 |
| hits | 2 |
| hive | 2 |
| hmu | 2 |
| holiday | 2 |
| homework | 2 |
| homie | 2 |
| honor | 2 |
| hopefully | 2 |
| hopping | 2 |
| hottest | 2 |
| humanity | 2 |
| hurry | 2 |
| hurt | 2 |
| ibaka | 2 |
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| tasted | 1 |
| tastings | 1 |
| tasty | 1 |
| tat | 1 |
| tattcity | 1 |
| tatted | 1 |
| tattooed | 1 |
| tattoos | 1 |
| taught | 1 |
| taurus | 1 |
| tay_bay10 | 1 |
| taylorvermilyea | 1 |
| tba | 1 |
| tbhjuststop | 1 |
| tcobryce | 1 |
| teacher | 1 |
| teachers | 1 |
| teagsssxo | 1 |
| teambortles | 1 |
| tebrik | 1 |
| tedrysz3 | 1 |
| tee | 1 |
| teen | 1 |
| teleprompter | 1 |
| temperature | 1 |
| temptation | 1 |
| tempting | 1 |
| tenders | 1 |
| tennessee | 1 |
| tennis | 1 |
| tensie | 1 |
| tent | 1 |
| terms | 1 |
| terrified | 1 |
| terrybdattd | 1 |
| tessktracy | 1 |
| testicles | 1 |
| tests | 1 |
| texas | 1 |
| texting | 1 |
| tfln | 1 |
| tgis | 1 |
| thaat | 1 |
| thang | 1 |
| thankyousoldiers | 1 |
| that'd | 1 |
| that70sreacts | 1 |
| thatisthequestion | 1 |
| thats_tb | 1 |
| the_realgerald | 1 |
| thecookierojas | 1 |
| thedanburruto | 1 |
| theejohnnyc | 1 |
| thefallen | 1 |
| thefunnyvine | 1 |
| thehinds222 | 1 |
| theirs | 1 |
| thelife | 1 |
| themed | 1 |
| thenameispax | 1 |
| theratsnake | 1 |
| thereal_dielon | 1 |
| thereal_trizzy | 1 |
| therealdicholas | 1 |
| therealmasaed23 | 1 |
| theredhead__ | 1 |
| theresa | 1 |
| therock585 | 1 |
| theroser | 1 |
| thetumblrposts | 1 |
| theweirdworld | 1 |
| they'll | 1 |
| they've | 1 |
| theyknow | 1 |
| they’re | 1 |
| thinmint | 1 |
| thirst | 1 |
| thirstythursdays | 1 |
| thirstythurzz | 1 |
| thiscouldbeusbut | 1 |
| thisisblumer | 1 |
| thomas | 1 |
| thomas_lada | 1 |
| thorny | 1 |
| threat | 1 |
| thro | 1 |
| throat | 1 |
| thrower | 1 |
| throws | 1 |
| thug | 1 |
| thunderstorm | 1 |
| thunderstorms | 1 |
| thurs | 1 |
| thus | 1 |
| thx | 1 |
| thy | 1 |
| tied | 1 |
| tierrraaaaa | 1 |
| tiffany | 1 |
| tiffy | 1 |
| tiffylau | 1 |
| tiki | 1 |
| tilt | 1 |
| timduncan | 1 |
| timehop | 1 |
| timeline | 1 |
| timer | 1 |
| timschillling | 1 |
| tindering | 1 |
| tinkled | 1 |
| tip | 1 |
| tippin | 1 |
| tiramisu | 1 |
| tissues | 1 |
| tjlawfirm | 1 |
| tmr | 1 |
| tmrrw | 1 |
| tmw | 1 |
| toad | 1 |
| tobytobessharp | 1 |
| toilet | 1 |
| tokyo | 1 |
| tolerance | 1 |
| tommorrow | 1 |
| tongue | 1 |
| tonic | 1 |
| tonipjs | 1 |
| tony | 1 |
| tonyclapper | 1 |
| tool | 1 |
| toomanyflowers | 1 |
| tooooo | 1 |
| tooshot | 1 |
| topdog | 1 |
| topper | 1 |
| toppings | 1 |
| tops | 1 |
| topsfriendlymarket | 1 |
| tori | 1 |
| torso | 1 |
| tossed | 1 |
| totally | 1 |
| tourney | 1 |
| towelthetank | 1 |
| tower | 1 |
| townofbrighton | 1 |
| tractor | 1 |
| tracy | 1 |
| tradition | 1 |
| trailer | 1 |
| trails | 1 |
| trainer | 1 |
| training | 1 |
| trains | 1 |
| traintowin | 1 |
| trap | 1 |
| travis | 1 |
| treat | 1 |
| treated | 1 |
| trebek | 1 |
| tree | 1 |
| trees | 1 |
| treleaven | 1 |
| trellpanye | 1 |
| trending | 1 |
| trenzalew | 1 |
| tries | 1 |
| trigger | 1 |
| trillavised | 1 |
| trim_zilla | 1 |
| trista | 1 |
| trivia | 1 |
| troubles | 1 |
| trutvjokers | 1 |
| truuuuu | 1 |
| tryin | 1 |
| tryjms | 1 |
| trynna | 1 |
| tschumann8 | 1 |
| tullamore | 1 |
| tullamoredew | 1 |
| tullyontour | 1 |
| tullys | 1 |
| tummy | 1 |
| tuned | 1 |
| turf | 1 |
| turndownforrick | 1 |
| turned | 1 |
| turns | 1 |
| turnupmarcela | 1 |
| tushay | 1 |
| tweaking | 1 |
| tweeter | 1 |
| tweeting | 1 |
| twenty | 1 |
| twerking | 1 |
| twist | 1 |
| twister | 1 |
| twitterless | 1 |
| twitterlesskelsey | 1 |
| twitterlessmegs | 1 |
| twohearted | 1 |
| ty's | 1 |
| tycho | 1 |
| tyler_smith56 | 1 |
| typing | 1 |
| tyranny | 1 |
| uak | 1 |
| uberfacts | 1 |
| ucc | 1 |
| uclfinal | 1 |
| ucsb | 1 |
| ufocus | 1 |
| ughh | 1 |
| ughhh | 1 |
| ugliest | 1 |
| uhhh | 1 |
| uinta | 1 |
| uintabrewing | 1 |
| uk | 1 |
| um | 1 |
| umbrella | 1 |
| umm | 1 |
| unable | 1 |
| undercover | 1 |
| underneath | 1 |
| undisputed | 1 |
| unexplained | 1 |
| unfollowed | 1 |
| unibrow | 1 |
| unity | 1 |
| universe | 1 |
| university | 1 |
| unless | 1 |
| unlucky | 1 |
| unr3voked_music | 1 |
| unreal | 1 |
| upgrade | 1 |
| upper | 1 |
| ups | 1 |
| upstateny | 1 |
| urinal | 1 |
| uses | 1 |
| uuugly | 1 |
| vag | 1 |
| val | 1 |
| valley | 1 |
| values | 1 |
| vamp | 1 |
| vanessaterrigno | 1 |
| vangaalmufc | 1 |
| vanilla | 1 |
| vanillins | 1 |
| vaping | 1 |
| ve | 1 |
| veil | 1 |
| vent | 1 |
| venues | 1 |
| version | 1 |
| veryharddecisions | 1 |
| vesile | 1 |
| vesper | 1 |
| vest | 1 |
| veteran | 1 |
| vibe | 1 |
| vibes | 1 |
| vic_consoles | 1 |
| victim's | 1 |
| victoire | 1 |
| victor | 1 |
| victorious | 1 |
| victorjameszz | 1 |
| victorybeer | 1 |
| view | 1 |
| viewing | 1 |
| villabellangelo | 1 |
| village | 1 |
| vines | 1 |
| vinnie | 1 |
| violate | 1 |
| vip | 1 |
| virginia | 1 |
| visitme | 1 |
| vitamins | 1 |
| vitomanuel97 | 1 |
| voice | 1 |
| volume | 1 |
| vorst | 1 |
| voters | 1 |
| votes | 1 |
| voting | 1 |
| voynov | 1 |
| vs | 1 |
| vu | 1 |
| vulgar | 1 |
| wade | 1 |
| wade's | 1 |
| wads86 | 1 |
| wages | 1 |
| wagner | 1 |
| waits | 1 |
| waka | 1 |
| wakaa24 | 1 |
| wakes | 1 |
| waking | 1 |
| waldo's | 1 |
| waldos | 1 |
| walkingstickmtn | 1 |
| wallst585 | 1 |
| walsh | 1 |
| wana | 1 |
| wanting | 1 |
| wanyizzle | 1 |
| wapatula | 1 |
| warm | 1 |
| warming | 1 |
| warmups | 1 |
| warped | 1 |
| wars | 1 |
| washed | 1 |
| washingtonpost | 1 |
| wat | 1 |
| watched | 1 |
| watches | 1 |
| watchgavwork | 1 |
| waterloo | 1 |
| watex | 1 |
| watkins | 1 |
| wave | 1 |
| wavy | 1 |
| wayfarers | 1 |
| ways | 1 |
| wbspenguins | 1 |
| wby | 1 |
| we'd | 1 |
| we've | 1 |
| wearemypet | 1 |
| weave | 1 |
| weaver | 1 |
| webb | 1 |
| website | 1 |
| wed | 1 |
| wedge | 1 |
| wee | 1 |
| weeding | 1 |
| weedsportmoose | 1 |
| weeknd | 1 |
| weeping | 1 |
| weight | 1 |
| weirder | 1 |
| weirdly | 1 |
| weisse | 1 |
| welcomehome | 1 |
| wellimm | 1 |
| wells | 1 |
| welp | 1 |
| wen | 1 |
| werk | 1 |
| western | 1 |
| westside | 1 |
| wet | 1 |
| wetm | 1 |
| wgathletics | 1 |
| whaaat | 1 |
| whaddup_breezy | 1 |
| wham | 1 |
| whammy | 1 |
| whatgoeson | 1 |
| whatiswilliamsburg | 1 |
| whatta | 1 |
| whatttt | 1 |
| whether | 1 |
| whip | 1 |
| whispers | 1 |
| whit | 1 |
| whitehouse | 1 |
| whitest | 1 |
| whitney_brand | 1 |
| whoaa | 1 |
| whoevers | 1 |
| whoops | 1 |
| whopper | 1 |
| whore | 1 |
| why'd | 1 |
| whyme | 1 |
| wide | 1 |
| wided | 1 |
| widn | 1 |
| wif | 1 |
| wifey | 1 |
| wih | 1 |
| wild | 1 |
| wildfox34 | 1 |
| wildkids | 1 |
| williambathurs1 | 1 |
| willingham’s | 1 |
| willwemakeitouttonight | 1 |
| wilson | 1 |
| wineandherbfest | 1 |
| winedrunk | 1 |
| wineries | 1 |
| wines | 1 |
| winetour | 1 |
| winetourproblems | 1 |
| winner | 1 |
| winners | 1 |
| winter | 1 |
| wipe | 1 |
| wipes | 1 |
| wiping | 1 |
| wishes | 1 |
| wishing | 1 |
| within | 1 |
| wittekerke | 1 |
| wiz | 1 |
| wjere | 1 |
| wkbandfan | 1 |
| woahhhkennie | 1 |
| wob | 1 |
| wob_usa | 1 |
| wobble | 1 |
| wobsyracuse | 1 |
| woelfelz | 1 |
| woken | 1 |
| wolf's | 1 |
| women's | 1 |
| wondered | 1 |
| wonderfully | 1 |
| wonderin | 1 |
| wont | 1 |
| wood | 1 |
| woodchucks | 1 |
| woodcrest | 1 |
| woodstock | 1 |
| woody's | 1 |
| wooohoooo | 1 |
| wordofadvice | 1 |
| words | 1 |
| wore | 1 |
| worked | 1 |
| worker | 1 |
| world's | 1 |
| worldstarghetto | 1 |
| worldstarvlnes | 1 |
| worldviewdawson | 1 |
| worries | 1 |
| worry | 1 |
| wouldve | 1 |
| wowww | 1 |
| wrestled | 1 |
| wrestling | 1 |
| wrist | 1 |
| write | 1 |
| written | 1 |
| wut | 1 |
| wwe | 1 |
| xander | 1 |
| xd | 1 |
| xmen | 1 |
| ya'll | 1 |
| yaaaasssss | 1 |
| yac | 1 |
| yacht | 1 |
| yang | 1 |
| yankee | 1 |
| yankeefanatic7 | 1 |
| yard | 1 |
| yarder | 1 |
| yasss | 1 |
| yay | 1 |
| yaycortland | 1 |
| yayo | 1 |
| ybg | 1 |
| yea | 1 |
| yea_buddy9 | 1 |
| yekratsnayr_ | 1 |
| yeo_justin | 1 |
| yerrp | 1 |
| yesallwomen | 1 |
| yessir | 1 |
| yoga | 1 |
| yogapantsqueen | 1 |
| yonce | 1 |
| youneverknow | 1 |
| youngboycey | 1 |
| youngest | 1 |
| yourselves | 1 |
| yrs | 1 |
| yuengling | 1 |
| yummmm | 1 |
| yummy | 1 |
| yummylaflare | 1 |
| yungwoo23 | 1 |
| yur | 1 |
| yuss | 1 |
| yyzemily | 1 |
| z955 | 1 |
| zach's | 1 |
| zackgruka | 1 |
| zak_lawlor | 1 |
| zakbeutel | 1 |
| zdsnyder | 1 |
| zeppelin | 1 |
| zero | 1 |
| zhed88 | 1 |
| zhotze | 1 |
| zombiemariii | 1 |
| zugibe | 1 |
We see a smiliar pattern when we include bigrams along with unigrams.
tokens_bigram |>
count(word, sort = TRUE) |>
slice(1:1000) |>
mutate(word = reorder(word, -n)) |>
ggplot(aes(word, n)) +
geom_col() +
xlab('Words') +
ylab('Raw Count') +
theme(axis.text.x=element_blank(),
axis.ticks.x=element_blank())
Display the total number of unique bigram tokens used across these tweets. There are a bunch of frequent ones that are drinking related.
tokens_bigram |>
filter(str_detect(word, " ")) |>
count(word, sort = TRUE) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| word | n |
|---|---|
| fucked up | 171 |
| turn up | 81 |
| in the | 70 |
| drinking a | 69 |
| at the | 63 |
| i'm at | 61 |
| ny sharedlink | 61 |
| on the | 57 |
| to the | 55 |
| of the | 45 |
| a beer | 44 |
| i was | 41 |
| a bottle | 39 |
| have a | 39 |
| bottle of | 38 |
| for the | 38 |
| i just | 38 |
| a party | 37 |
| the bar | 36 |
| and i | 35 |
| to get | 35 |
| get drunk | 34 |
| my life | 34 |
| i need | 33 |
| of wine | 33 |
| i have | 32 |
| in my | 32 |
| rochester ny | 32 |
| going to | 31 |
| in a | 30 |
| on a | 30 |
| a shot | 28 |
| for a | 27 |
| i don't | 27 |
| last night | 27 |
| the party | 27 |
| with my | 27 |
| with the | 26 |
| beer and | 25 |
| country club | 25 |
| i want | 25 |
| pub sharedlink | 25 |
| and a | 24 |
| bar b | 24 |
| dinosaur bar | 24 |
| i can | 24 |
| i love | 24 |
| new music | 24 |
| the club | 24 |
| when i | 24 |
| with a | 24 |
| 1st shot | 23 |
| b que | 23 |
| by phreshvision | 23 |
| have to | 23 |
| if i | 23 |
| is a | 23 |
| j.clancy's new | 23 |
| june 1st | 23 |
| life releases | 23 |
| music video | 23 |
| of a | 23 |
| on my | 23 |
| releases june | 23 |
| titled my | 23 |
| video titled | 23 |
| want to | 22 |
| amp directed | 21 |
| directed by | 21 |
| getting drunk | 21 |
| go to | 21 |
| of my | 21 |
| phreshvision sharedlink | 21 |
| right now | 21 |
| shot amp | 21 |
| to a | 21 |
| bar amp | 20 |
| i'm drunk | 20 |
| to be | 20 |
| up my | 20 |
| club sharedlink | 19 |
| drunk and | 19 |
| i am | 19 |
| if you | 19 |
| me and | 19 |
| with me | 19 |
| i think | 18 |
| need a | 18 |
| to my | 18 |
| when you | 18 |
| at my | 17 |
| it's a | 17 |
| need to | 17 |
| others sharedlink | 17 |
| out of | 17 |
| so much | 17 |
| the best | 17 |
| bar sharedlink | 16 |
| for me | 16 |
| i can't | 16 |
| i get | 16 |
| i got | 16 |
| is the | 16 |
| like a | 16 |
| to turn | 16 |
| trying to | 16 |
| will be | 16 |
| bar and | 15 |
| i had | 15 |
| me to | 15 |
| my mom | 15 |
| so fucked | 15 |
| up on | 15 |
| you can | 15 |
| a bar | 14 |
| a good | 14 |
| all the | 14 |
| and i'm | 14 |
| feel like | 14 |
| me a | 14 |
| my dad | 14 |
| shot of | 14 |
| syracuse ny | 14 |
| that i | 14 |
| a great | 13 |
| athletic club | 13 |
| glass of | 13 |
| got a | 13 |
| got me | 13 |
| i wanna | 13 |
| i'm not | 13 |
| it was | 13 |
| my house | 13 |
| my party | 13 |
| so i | 13 |
| this weekend | 13 |
| a lot | 12 |
| and beer | 12 |
| i'm so | 12 |
| irish pub | 12 |
| is so | 12 |
| one of | 12 |
| party tonight | 12 |
| take a | 12 |
| that was | 12 |
| this is | 12 |
| what a | 12 |
| wine and | 12 |
| a little | 11 |
| all i | 11 |
| amp grill | 11 |
| at a | 11 |
| beer sharedlink | 11 |
| bud light | 11 |
| but i | 11 |
| drunk in | 11 |
| end of | 11 |
| had a | 11 |
| how to | 11 |
| i will | 11 |
| that shot | 11 |
| this party | 11 |
| time to | 11 |
| to go | 11 |
| to party | 11 |
| to see | 11 |
| too drunk | 11 |
| turnt up | 11 |
| up in | 11 |
| water bottle | 11 |
| when i'm | 11 |
| wine tour | 11 |
| a fucked | 10 |
| and my | 10 |
| be a | 10 |
| can't wait | 10 |
| for my | 10 |
| from the | 10 |
| grad party | 10 |
| gt gt | 10 |
| having a | 10 |
| i feel | 10 |
| i hate | 10 |
| i know | 10 |
| i've been | 10 |
| my phone | 10 |
| of beer | 10 |
| party at | 10 |
| root beer | 10 |
| screen shot | 10 |
| shot at | 10 |
| still drunk | 10 |
| strip club | 10 |
| thank you | 10 |
| the liquor | 10 |
| to do | 10 |
| up with | 10 |
| wanna get | 10 |
| you get | 10 |
| you know | 10 |
| ale by | 9 |
| and the | 9 |
| club rochester | 9 |
| drinking an | 9 |
| golf club | 9 |
| i fucked | 9 |
| i'm a | 9 |
| ipa by | 9 |
| is fucked | 9 |
| join the | 9 |
| like i | 9 |
| me fucked | 9 |
| ny w | 9 |
| of water | 9 |
| party and | 9 |
| such a | 9 |
| that's fucked | 9 |
| time i | 9 |
| to have | 9 |
| to make | 9 |
| was drunk | 9 |
| welcome to | 9 |
| would be | 9 |
| 2 others | 8 |
| a glass | 8 |
| a new | 8 |
| all night | 8 |
| and get | 8 |
| and it | 8 |
| and wine | 8 |
| as fuck | 8 |
| beer with | 8 |
| bout to | 8 |
| by the | 8 |
| cinco de | 8 |
| cold beer | 8 |
| de mayo | 8 |
| do it | 8 |
| drinking beer | 8 |
| drunk as | 8 |
| drunk but | 8 |
| drunk on | 8 |
| drunk to | 8 |
| get a | 8 |
| get fucked | 8 |
| gonna be | 8 |
| had to | 8 |
| have the | 8 |
| i do | 8 |
| i wasted | 8 |
| i wish | 8 |
| i'm getting | 8 |
| i'm just | 8 |
| just want | 8 |
| liquor store | 8 |
| listening to | 8 |
| lt lt | 8 |
| needs to | 8 |
| off the | 8 |
| party is | 8 |
| party this | 8 |
| party with | 8 |
| que dinobbq | 8 |
| the day | 8 |
| the end | 8 |
| the most | 8 |
| the one | 8 |
| the only | 8 |
| this morning | 8 |
| to come | 8 |
| to take | 8 |
| up and | 8 |
| up to | 8 |
| up tonight | 8 |
| w 2 | 8 |
| was a | 8 |
| was so | 8 |
| was the | 8 |
| way to | 8 |
| we are | 8 |
| who wants | 8 |
| wine tasting | 8 |
| world of | 8 |
| about the | 7 |
| after party | 7 |
| all of | 7 |
| am i | 7 |
| are drunk | 7 |
| are you | 7 |
| as a | 7 |
| at dinosaur | 7 |
| better than | 7 |
| birthday party | 7 |
| breakfast club | 7 |
| brew pub | 7 |
| come on | 7 |
| don't have | 7 |
| drunk or | 7 |
| get me | 7 |
| get my | 7 |
| get shot | 7 |
| had the | 7 |
| i hope | 7 |
| i wonder | 7 |
| i would | 7 |
| i'll be | 7 |
| i'm going | 7 |
| in this | 7 |
| instead of | 7 |
| is my | 7 |
| is not | 7 |
| just a | 7 |
| kind of | 7 |
| lager by | 7 |
| let's get | 7 |
| love you | 7 |
| new york | 7 |
| no one | 7 |
| not a | 7 |
| party in | 7 |
| party like | 7 |
| party on | 7 |
| party or | 7 |
| ready to | 7 |
| shot to | 7 |
| talking about | 7 |
| tea party | 7 |
| that is | 7 |
| the fact | 7 |
| the way | 7 |
| the world | 7 |
| think i | 7 |
| this summer | 7 |
| to work | 7 |
| up for | 7 |
| wait to | 7 |
| wants to | 7 |
| wish i | 7 |
| woke up | 7 |
| you are | 7 |
| you guys | 7 |
| you have | 7 |
| a bad | 6 |
| a day | 6 |
| a few | 6 |
| a girl | 6 |
| about to | 6 |
| acme bar | 6 |
| all fucked | 6 |
| amp pizza | 6 |
| and it's | 6 |
| and now | 6 |
| and some | 6 |
| at o'callaghan's | 6 |
| at this | 6 |
| bad girls | 6 |
| be in | 6 |
| beer by | 6 |
| beer in | 6 |
| beer is | 6 |
| before i | 6 |
| come to | 6 |
| coming to | 6 |
| dinobbq sharedlink | 6 |
| drunk with | 6 |
| for this | 6 |
| get wasted | 6 |
| got shot | 6 |
| grill sharedlink | 6 |
| happy birthday | 6 |
| has a | 6 |
| have no | 6 |
| he was | 6 |
| hit a | 6 |
| i are | 6 |
| i like | 6 |
| i really | 6 |
| i still | 6 |
| i'm gonna | 6 |
| i'm still | 6 |
| in love | 6 |
| is going | 6 |
| is it | 6 |
| it all | 6 |
| it's not | 6 |
| jump shot | 6 |
| just got | 6 |
| just wanna | 6 |
| like the | 6 |
| lot of | 6 |
| made it | 6 |
| makes me | 6 |
| me in | 6 |
| me up | 6 |
| my car | 6 |
| my family | 6 |
| my new | 6 |
| now i'm | 6 |
| o'callaghan's pub | 6 |
| ocallaghanpub rochester | 6 |
| party i | 6 |
| party sharedlink | 6 |
| pizza sharedlink | 6 |
| pool party | 6 |
| pub ocallaghanpub | 6 |
| pub w | 6 |
| rochester athletic | 6 |
| she was | 6 |
| shot by | 6 |
| shot in | 6 |
| shot on | 6 |
| shot today | 6 |
| so drunk | 6 |
| so good | 6 |
| so i'm | 6 |
| that party | 6 |
| the same | 6 |
| the time | 6 |
| the worst | 6 |
| to me | 6 |
| told me | 6 |
| up sharedlink | 6 |
| wine amp | 6 |
| wine is | 6 |
| you and | 6 |
| you fucked | 6 |
| you i | 6 |
| you to | 6 |
| 3 others | 5 |
| a big | 5 |
| a drunk | 5 |
| a wine | 5 |
| after work | 5 |
| all day | 5 |
| all my | 5 |
| all over | 5 |
| amp bar | 5 |
| amp i | 5 |
| an alcoholic | 5 |
| and drink | 5 |
| and then | 5 |
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| my parents | 4 |
| my sister | 4 |
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| nothing but | 4 |
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| of me | 4 |
| of that | 4 |
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| on you | 4 |
| one and | 4 |
| or a | 4 |
| or nah | 4 |
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| park and | 4 |
| party girls | 4 |
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| peach beer | 4 |
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| probably the | 4 |
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| rochester midtown_roc | 4 |
| rose eddies | 4 |
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| smells like | 4 |
| so shot | 4 |
| some wine | 4 |
| sorts of | 4 |
| sports bar | 4 |
| sunshine and | 4 |
| syracuseu syracuse | 4 |
| text me | 4 |
| that means | 4 |
| the big | 4 |
| the breakfast | 4 |
| the face | 4 |
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| the greatest | 4 |
| the other | 4 |
| the real | 4 |
| the red | 4 |
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| too much | 4 |
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| water bottles | 4 |
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| what i | 4 |
| when a | 4 |
| when my | 4 |
| when she | 4 |
| where the | 4 |
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| year old | 4 |
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| you a | 4 |
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| you don't | 4 |
| your beer | 4 |
| zoo brew | 4 |
| 3 bottles | 3 |
| 5 others | 3 |
| 78 pub | 3 |
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| a bunch | 3 |
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| getting my | 3 |
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| here in | 3 |
| high school | 3 |
| holy fuck | 3 |
| hope you | 3 |
| how do | 3 |
| how you | 3 |
| i came | 3 |
| i drink | 3 |
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| i forgot | 3 |
| i gave | 3 |
| i hear | 3 |
| i knew | 3 |
| i mean | 3 |
| i might | 3 |
| i put | 3 |
| i remember | 3 |
| i shot | 3 |
| i went | 3 |
| i'll buy | 3 |
| i'll have | 3 |
| i'm home | 3 |
| i'm on | 3 |
| i'm out | 3 |
| i'm ready | 3 |
| i'm really | 3 |
| i'm such | 3 |
| i'm tipsy | 3 |
| if i'm | 3 |
| im not | 3 |
| in bottles | 3 |
| in her | 3 |
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| in syracuse | 3 |
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| is life | 3 |
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| is playing | 3 |
| is shot | 3 |
| is what | 3 |
| it doesn't | 3 |
| it for | 3 |
| it i'm | 3 |
| it is | 3 |
| it to | 3 |
| it up | 3 |
| it's just | 3 |
| it's my | 3 |
| it's time | 3 |
| ithaca ny | 3 |
| its a | 3 |
| join my | 3 |
| just go | 3 |
| just going | 3 |
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| jyolevich sharedlink | 3 |
| kiss me | 3 |
| klondike bar | 3 |
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| late night | 3 |
| let's go | 3 |
| let's take | 3 |
| life is | 3 |
| like it's | 3 |
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| lol the | 3 |
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| me tonight | 3 |
| me too | 3 |
| me want | 3 |
| mirage baja | 3 |
| miss you | 3 |
| mom is | 3 |
| more drinks | 3 |
| mtn_dew no | 3 |
| much of | 3 |
| music and | 3 |
| my back | 3 |
| my bottle | 3 |
| my brother | 3 |
| my face | 3 |
| my girl | 3 |
| my god | 3 |
| my grad | 3 |
| my graduation | 3 |
| my hangover | 3 |
| my head | 3 |
| my memory | 3 |
| my name | 3 |
| my niggas | 3 |
| my sleep | 3 |
| my teeth | 3 |
| my whole | 3 |
| my wine | 3 |
| need of | 3 |
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| next week | 3 |
| nice cold | 3 |
| night and | 3 |
| night of | 3 |
| night with | 3 |
| no more | 3 |
| no this | 3 |
| not getting | 3 |
| not my | 3 |
| not the | 3 |
| not to | 3 |
| now a | 3 |
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| now that | 3 |
| now you | 3 |
| of alcohol | 3 |
| of binge | 3 |
| of course | 3 |
| of henny | 3 |
| of one | 3 |
| of rum | 3 |
| of tequila | 3 |
| of vodka | 3 |
| of whiskey | 3 |
| ok i'm | 3 |
| on 390 | 3 |
| on bad | 3 |
| on beer | 3 |
| on myself | 3 |
| on our | 3 |
| on your | 3 |
| once again | 3 |
| one beer | 3 |
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| only sharedlink | 3 |
| only thing | 3 |
| or is | 3 |
| or just | 3 |
| or the | 3 |
| out a | 3 |
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| out here | 3 |
| over the | 3 |
| park zoo | 3 |
| part of | 3 |
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| party city | 3 |
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| party pooper | 3 |
| party tomorrow | 3 |
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| phone is | 3 |
| picking up | 3 |
| posted a | 3 |
| pretty much | 3 |
| pub amp | 3 |
| put in | 3 |
| put it | 3 |
| racquet club | 3 |
| ready for | 3 |
| really turnt | 3 |
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| restaurant amp | 3 |
| right in | 3 |
| room sharedlink | 3 |
| route 78 | 3 |
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| see me | 3 |
| seneca park | 3 |
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| she's drunk | 3 |
| shit faced | 3 |
| shit i | 3 |
| shot from | 3 |
| shot my | 3 |
| shot that | 3 |
| shot with | 3 |
| shot you | 3 |
| should not | 3 |
| since i | 3 |
| sit here | 3 |
| smoke weed | 3 |
| so many | 3 |
| so my | 3 |
| so what | 3 |
| some people | 3 |
| sounds like | 3 |
| splitrock brew | 3 |
| stafford country | 3 |
| stand for | 3 |
| summer is | 3 |
| summerofbaja only | 3 |
| supposed to | 3 |
| talk to | 3 |
| taste like | 3 |
| tavern sharedlink | 3 |
| tell me | 3 |
| tell you | 3 |
| texted me | 3 |
| thanks to | 3 |
| that i'm | 3 |
| that my | 3 |
| that one | 3 |
| that shit | 3 |
| the amount | 3 |
| the back | 3 |
| the bottles | 3 |
| the boys | 3 |
| the car | 3 |
| the cause | 3 |
| the country | 3 |
| the crown | 3 |
| the evening | 3 |
| the front | 3 |
| the game | 3 |
| the genesee | 3 |
| the good | 3 |
| the land | 3 |
| the mall | 3 |
| the moment | 3 |
| the morning | 3 |
| the next | 3 |
| the radio | 3 |
| the senior | 3 |
| the summerofbaja | 3 |
| the sun | 3 |
| the taste | 3 |
| the vodka | 3 |
| the week | 3 |
| the wine | 3 |
| the wrong | 3 |
| there's nothing | 3 |
| these bitches | 3 |
| they have | 3 |
| things that | 3 |
| this afternoon | 3 |
| this place | 3 |
| this semester | 3 |
| this wine | 3 |
| thought i | 3 |
| time and | 3 |
| time i've | 3 |
| time we | 3 |
| to ask | 3 |
| to bed | 3 |
| to buy | 3 |
| to eat | 3 |
| to find | 3 |
| to hit | 3 |
| to kill | 3 |
| to open | 3 |
| to play | 3 |
| to put | 3 |
| to show | 3 |
| to talk | 3 |
| to tell | 3 |
| to wake | 3 |
| to you | 3 |
| tomorrow morning | 3 |
| tonight i | 3 |
| tonight is | 3 |
| took my | 3 |
| triple crown | 3 |
| type of | 3 |
| until i | 3 |
| up all | 3 |
| up is | 3 |
| up like | 3 |
| up since | 3 |
| up tomorrow | 3 |
| used to | 3 |
| vodka and | 3 |
| w 5 | 3 |
| w jyolevich | 3 |
| wanna come | 3 |
| wanna do | 3 |
| want is | 3 |
| want me | 3 |
| wanted to | 3 |
| was going | 3 |
| was that | 3 |
| was too | 3 |
| wasted and | 3 |
| we can | 3 |
| we drink | 3 |
| we stand | 3 |
| we will | 3 |
| weed get | 3 |
| went from | 3 |
| what else | 3 |
| what i'm | 3 |
| what if | 3 |
| when it's | 3 |
| when we | 3 |
| when you're | 3 |
| when your | 3 |
| where i | 3 |
| where's the | 3 |
| whiskey and | 3 |
| why does | 3 |
| why the | 3 |
| will never | 3 |
| wine cellars | 3 |
| wine chocolate | 3 |
| wine in | 3 |
| wine would | 3 |
| with some | 3 |
| with that | 3 |
| women sharedlink | 3 |
| wonder how | 3 |
| worst thing | 3 |
| would you | 3 |
| wouldn't be | 3 |
| wrong people | 3 |
| wrong with | 3 |
| xbox and | 3 |
| years ago | 3 |
| you come | 3 |
| you go | 3 |
| you just | 3 |
| you need | 3 |
| you should | 3 |
| you up | 3 |
| you wanna | 3 |
| you you | 3 |
| you're a | 3 |
| you're drunk | 3 |
| your shot | 3 |
| 1 beer | 2 |
| 10 30 | 2 |
| 12 hours | 2 |
| 12 percent | 2 |
| 140 alex | 2 |
| 20 minutes | 2 |
| 2014 by | 2 |
| 2014 wantuck | 2 |
| 2nd period | 2 |
| 30 on | 2 |
| 308 delaware | 2 |
| 31 bière | 2 |
| 4 0 | 2 |
| 4 2 | 2 |
| 5 minutes | 2 |
| 7 for | 2 |
| a 4 | 2 |
| a alcoholic | 2 |
| a berliner | 2 |
| a better | 2 |
| a bit | 2 |
| a black | 2 |
| a bus | 2 |
| a club | 2 |
| a cocktail | 2 |
| a cold | 2 |
| a couple | 2 |
| a crazy | 2 |
| a crown | 2 |
| a days | 2 |
| a discount | 2 |
| a dos | 2 |
| a drinking | 2 |
| a fuckin | 2 |
| a game | 2 |
| a genesee | 2 |
| a golf | 2 |
| a grad | 2 |
| a gun | 2 |
| a hangover | 2 |
| a joint | 2 |
| a la | 2 |
| a lonely | 2 |
| a loyal | 2 |
| a moment | 2 |
| a monopoly | 2 |
| a nap | 2 |
| a personal | 2 |
| a pool | 2 |
| a room | 2 |
| a samuel | 2 |
| a scotch | 2 |
| a scotty | 2 |
| a shower | 2 |
| a single | 2 |
| a terrible | 2 |
| a traditional | 2 |
| a turkey | 2 |
| a two | 2 |
| a very | 2 |
| a water | 2 |
| abandon brewing | 2 |
| abc party | 2 |
| about beer | 2 |
| about getting | 2 |
| about her | 2 |
| about life | 2 |
| about my | 2 |
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| lightskin32013 naishekirah | 2 |
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| m_c_b_f kristy_olthof | 2 |
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| ya shot | 1 |
| ya u | 1 |
| ya whole | 1 |
| ya'll look | 1 |
| yaaaasssss banana | 1 |
| yacht club | 1 |
| yah might | 1 |
| yak behind | 1 |
| yak game | 1 |
| yak here | 1 |
| yak in | 1 |
| yak is | 1 |
| yak mad | 1 |
| yak shall | 1 |
| yak that | 1 |
| yak train | 1 |
| yal life | 1 |
| yal need | 1 |
| yall are | 1 |
| yall feelings | 1 |
| yang greysfinale | 1 |
| yankee stadium | 1 |
| yankeefanatic7 jeffrey | 1 |
| yankees i | 1 |
| yankees to | 1 |
| yard work | 1 |
| yarder at | 1 |
| yards anyway | 1 |
| yards but | 1 |
| yasss turn | 1 |
| yayo dance | 1 |
| ybg innercitykid | 1 |
| yea girl | 1 |
| yea_buddy9 lmaoo | 1 |
| yeah about | 1 |
| yeah banger | 1 |
| yeah my | 1 |
| yeah that | 1 |
| yeah we'll | 1 |
| year ago | 1 |
| year and | 1 |
| year drunk | 1 |
| year have | 1 |
| year now | 1 |
| year on | 1 |
| year party | 1 |
| year parv | 1 |
| year supply | 1 |
| year with | 1 |
| year yo | 1 |
| year you | 1 |
| year younger | 1 |
| years and | 1 |
| years last | 1 |
| years of | 1 |
| years old | 1 |
| years or | 1 |
| years over | 1 |
| yekratsnayr_ hangover | 1 |
| yelling at | 1 |
| yelling while | 1 |
| yeo_justin for | 1 |
| yerrp drumlins | 1 |
| yes a | 1 |
| yes golf | 1 |
| yes honey | 1 |
| yes literally | 1 |
| yes please | 1 |
| yes so | 1 |
| yes to | 1 |
| yes votes | 1 |
| yessir positive | 1 |
| yesss are | 1 |
| yesss soon | 1 |
| yesterday for | 1 |
| yesterday hooping | 1 |
| yesterday my | 1 |
| yesterday was | 1 |
| yesterday yet | 1 |
| yet any | 1 |
| yet because | 1 |
| yet blu | 1 |
| yet bug | 1 |
| yet by | 1 |
| yet drinking | 1 |
| yet lmao | 1 |
| yet oh | 1 |
| yet wasn't | 1 |
| ymca sharedlink | 1 |
| yo are | 1 |
| yo ass | 1 |
| yo bridg3t_c0llins | 1 |
| yo christine | 1 |
| yo come | 1 |
| yo dad | 1 |
| yo i | 1 |
| yo join | 1 |
| yo leg | 1 |
| yo momma | 1 |
| yo my | 1 |
| yo party | 1 |
| yo sharedlink | 1 |
| yo the | 1 |
| yoga lessons | 1 |
| yogapantsqueen 2015lmcpredictions | 1 |
| yolo swag | 1 |
| yonce all | 1 |
| yoo i'm | 1 |
| york i_love_ny | 1 |
| york my | 1 |
| york on | 1 |
| york wine | 1 |
| you 1 | 1 |
| you agree | 1 |
| you all | 1 |
| you amp | 1 |
| you any | 1 |
| you apple | 1 |
| you asked | 1 |
| you available | 1 |
| you back | 1 |
| you bagging | 1 |
| you bday | 1 |
| you be | 1 |
| you beca | 1 |
| you better | 1 |
| you blacked | 1 |
| you bro | 1 |
| you carabbas | 1 |
| you coming | 1 |
| you cry | 1 |
| you day1 | 1 |
| you deleted | 1 |
| you different | 1 |
| you disappear | 1 |
| you dont | 1 |
| you downn | 1 |
| you drink | 1 |
| you drive | 1 |
| you ended | 1 |
| you face | 1 |
| you fall | 1 |
| you finish | 1 |
| you for | 1 |
| you free | 1 |
| you fuckin | 1 |
| you gettin | 1 |
| you girlproblems | 1 |
| you girrrrrl | 1 |
| you give | 1 |
| you gon | 1 |
| you gonna | 1 |
| you half | 1 |
| you hate | 1 |
| you hit | 1 |
| you how | 1 |
| you hungrier | 1 |
| you keith_ajohnson | 1 |
| you keithurban | 1 |
| you knock | 1 |
| you leave | 1 |
| you left | 1 |
| you like | 1 |
| you lol | 1 |
| you lose | 1 |
| you love | 1 |
| you mean | 1 |
| you might | 1 |
| you missed | 1 |
| you my | 1 |
| you myself | 1 |
| you natasha | 1 |
| you needa | 1 |
| you never | 1 |
| you now | 1 |
| you occ | 1 |
| you over | 1 |
| you puttin | 1 |
| you rangers | 1 |
| you realize | 1 |
| you really | 1 |
| you rejected | 1 |
| you remember | 1 |
| you right | 1 |
| you saw | 1 |
| you saying | 1 |
| you score | 1 |
| you see | 1 |
| you sharedlink | 1 |
| you shot | 1 |
| you something | 1 |
| you still | 1 |
| you take | 1 |
| you that | 1 |
| you that's | 1 |
| you there | 1 |
| you think | 1 |
| you thinkin | 1 |
| you though | 1 |
| you tired | 1 |
| you turnt | 1 |
| you twerking | 1 |
| you ugly | 1 |
| you until | 1 |
| you use | 1 |
| you wait | 1 |
| you wasted | 1 |
| you we | 1 |
| you were | 1 |
| you will | 1 |
| you wine | 1 |
| you women's | 1 |
| you yah | 1 |
| you yours | 1 |
| you'd really | 1 |
| you'd think | 1 |
| you'll always | 1 |
| you're all | 1 |
| you're are | 1 |
| you're cute | 1 |
| you're fucking | 1 |
| you're having | 1 |
| you're missing | 1 |
| you're out | 1 |
| you're probably | 1 |
| you're really | 1 |
| you're shot | 1 |
| you're such | 1 |
| you're tipsy | 1 |
| you're with | 1 |
| you're wrong | 1 |
| you've had | 1 |
| you've seen | 1 |
| young and | 1 |
| young lady | 1 |
| young that's | 1 |
| young thug | 1 |
| youngboycey your | 1 |
| younger brothers | 1 |
| younger then | 1 |
| youngest at | 1 |
| your alcohol | 1 |
| your an | 1 |
| your best | 1 |
| your big | 1 |
| your blender | 1 |
| your blue | 1 |
| your bookbag | 1 |
| your boyfriend | 1 |
| your butts | 1 |
| your candidates | 1 |
| your chicks | 1 |
| your college | 1 |
| your combo | 1 |
| your coming | 1 |
| your crush | 1 |
| your damn | 1 |
| your daughters | 1 |
| your day | 1 |
| your dj | 1 |
| your doing | 1 |
| your drink | 1 |
| your drunk | 1 |
| your elbow | 1 |
| your fly | 1 |
| your four | 1 |
| your freedom | 1 |
| your frozen | 1 |
| your girl | 1 |
| your grad | 1 |
| your hand | 1 |
| your hardest | 1 |
| your heart | 1 |
| your id | 1 |
| your jordanian | 1 |
| your jump | 1 |
| your leading | 1 |
| your legal | 1 |
| your life | 1 |
| your liquor | 1 |
| your mail | 1 |
| your makeup | 1 |
| your mind | 1 |
| your mouth | 1 |
| your nose | 1 |
| your nostrils | 1 |
| your number | 1 |
| your parents | 1 |
| your parking | 1 |
| your perfume | 1 |
| your personal | 1 |
| your picking | 1 |
| your place | 1 |
| your pull | 1 |
| your rooms | 1 |
| your schedule | 1 |
| your shit | 1 |
| your sjp | 1 |
| your talkin | 1 |
| your the | 1 |
| your time | 1 |
| your twitter | 1 |
| your veil | 1 |
| your whiskey | 1 |
| your wine | 1 |
| your worries | 1 |
| youre a | 1 |
| youre coming | 1 |
| youre hot | 1 |
| yours since | 1 |
| yours sounds | 1 |
| yourself at | 1 |
| yourself cause | 1 |
| yourselves or | 1 |
| you’re down | 1 |
| you’re drunk | 1 |
| yr anniversary | 1 |
| yrs ago | 1 |
| yuengling went | 1 |
| yuengling_beer at | 1 |
| yuengling_beer dave | 1 |
| yum booze | 1 |
| yum dinosaur | 1 |
| yum sharedlink | 1 |
| yummmm marathontraining | 1 |
| yummy in | 1 |
| yummylaflare these | 1 |
| yungwoo23 if | 1 |
| yup i'm | 1 |
| yup you're | 1 |
| yur system | 1 |
| yyzemily several | 1 |
| z955 every | 1 |
| zach bull | 1 |
| zach that | 1 |
| zach to | 1 |
| zach was | 1 |
| zach's dad | 1 |
| zackgruka turn | 1 |
| zak_lawlor for | 1 |
| zakbeutel lets | 1 |
| zdsnyder woahhhkennie | 1 |
| zeppelin is | 1 |
| zero so | 1 |
| zhed88 cj_thehusky | 1 |
| zhotze get | 1 |
| zoo red | 1 |
| zugibe vineyards | 1 |
Final cleaning
Based on the EDA above, we apply cleaning steps to the full data set.
The only step that isn’t right to apply to the full data set will be filtering out N/A observations – I will only remove the N/A training observations.
cleaned_data <- data |>
# remove duplicated observations across the full data set
filter(!duplicated(id)) |>
# remove missing tweets from training data only
filter(!id %in% na_training_ids) |>
# replace links with sharedlink
mutate(text = str_replace(text, "(https?://[^/\\s]+)[^\\s]*", "sharedlink")) |>
mutate(text = str_replace(text, "sharedlink.*", "sharedlink")) |>
# remove all remaining punctuation
mutate(text = str_replace_all(text, "[[:punct:]]", "")) |>
# make all text lowercase
mutate(text = tolower(text)) |>
glimpse()Rows: 3,653
Columns: 3
$ id <dbl> 471711578795278336, 464553997081526272, 4680948221090365…
$ user_drinking <fct> no, yes, no, no, no, no, no, no, yes, yes, yes, yes, no,…
$ text <chr> "shigdollaz shot dollaz we got back wit ya fuck yo leg u…Resplit
Run this code chunk to apply the same split as we did at the beginning to the fully cleaned data. Make sure to use the same seed.
set.seed(12345)
splits <- cleaned_data |>
validation_split(strata = user_drinking)
training_ind <- splits |>
unlist(recursive = FALSE)
training_ind <- training_ind$splits$in_id
data_train_clean <- cleaned_data |>
slice(training_ind)Part 2: Model Building
On to considering model configurations! Let’s put together some stop words and save them in stops. We will call on these when we set up recipes.
stop_snowball <- get_stopwords(source = "snowball")
stop_smart <- get_stopwords(source = "smart")
stops <- union(pull(stop_snowball, word), pull(stop_smart, word))Unigram models
First, lets look at the performance of a unigram model with versus without stop words.
# set up recipes
rec_uni <- recipe(user_drinking ~ text, data = data_train_clean) |>
step_string2factor(user_drinking) |>
step_tokenize(text, engine = "tokenizers", token = "words") |>
step_tokenfilter(text, max_tokens = tune::tune()) |>
step_tfidf(text)
rec_uni_stop <- recipe(user_drinking ~ text, data = data_train_clean) |>
step_string2factor(user_drinking) |>
step_tokenize(text, engine = "tokenizers", token = "words",
options = list(stopwords = stops)) |>
step_tokenfilter(text, max_tokens = tune::tune()) |>
step_tfidf(text)
# set up grid
grid_tokens <- grid_regular(penalty(range = c(-7, 3)),
mixture(),
max_tokens(range = c(4000, 5000)),
levels = c(20, 6, 2))
# Plain unigram
fits_unigram <-
logistic_reg(penalty = tune::tune(),
mixture = tune::tune()) |>
set_engine("glmnet") |>
tune_grid(preprocessor = rec_uni,
resamples = splits,
grid = grid_tokens,
metrics = metric_set(bal_accuracy))
show_best(fits_unigram)Warning in show_best(fits_unigram): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 9
penalty mixture max_tokens .metric .estimator mean n std_err
<dbl> <dbl> <int> <chr> <chr> <dbl> <int> <dbl>
1 0.00546 1 4000 bal_accuracy binary 0.735 1 NA
2 0.00546 1 5000 bal_accuracy binary 0.730 1 NA
3 0.00546 0.8 4000 bal_accuracy binary 0.730 1 NA
4 0.00546 0.8 5000 bal_accuracy binary 0.727 1 NA
5 0.0183 0.4 5000 bal_accuracy binary 0.726 1 NA
.config
<chr>
1 Preprocessor1_Model110
2 Preprocessor2_Model110
3 Preprocessor1_Model090
4 Preprocessor2_Model090
5 Preprocessor2_Model051Our best model is performing at 73.5% accuracy. We definitely find a maximum over our hyperparameters - always nice to see a clear winner in these plots.
autoplot(fits_unigram)
This configuration doesn’t use stopwords. We will add them in and see if that improves things.
# Unigram with stopwords
fits_unigram_stop <-
logistic_reg(penalty = tune::tune(),
mixture = tune::tune()) |>
set_engine("glmnet") |>
tune_grid(preprocessor = rec_uni_stop,
resamples = splits,
grid = grid_tokens,
metrics = metric_set(bal_accuracy))
show_best(fits_unigram_stop)Warning in show_best(fits_unigram_stop): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 9
penalty mixture max_tokens .metric .estimator mean n std_err
<dbl> <dbl> <int> <chr> <chr> <dbl> <int> <dbl>
1 0.0000001 1 5000 bal_accuracy binary 0.728 1 NA
2 0.000000336 1 5000 bal_accuracy binary 0.728 1 NA
3 0.00000113 1 5000 bal_accuracy binary 0.728 1 NA
4 0.00000379 1 5000 bal_accuracy binary 0.728 1 NA
5 0.0000127 1 5000 bal_accuracy binary 0.728 1 NA
.config
<chr>
1 Preprocessor2_Model101
2 Preprocessor2_Model102
3 Preprocessor2_Model103
4 Preprocessor2_Model104
5 Preprocessor2_Model105On splits, our model without stopwords (73.5%) is slightly outperforming the one with stopwords (72.8% accuracy). Again we hit our global maximum for tuning, seen below.
autoplot(fits_unigram_stop)
We will run a model with ngrams (unigram and bigram) and see how we do there, again both with and without stopwords.
rec_ngram <- recipe(user_drinking ~ text, data = data_train_clean) |>
step_string2factor(user_drinking) |>
step_tokenize(text, engine = "tokenizers", token = "ngrams",
options = list(n = 2, n_min = 1)) |>
step_tokenfilter(text, max_tokens = tune::tune()) |>
step_tfidf(text) |>
step_normalize(all_predictors())
fits_ngram <-
logistic_reg(penalty = tune::tune(),
mixture = tune::tune()) |>
set_engine("glmnet") |>
tune_grid(preprocessor = rec_ngram,
resamples = splits,
grid = grid_tokens,
metrics = metric_set(bal_accuracy))We are getting the best model that we can out of these hyperparameters. But we can’t yet beat our first model, here getting 72.6% accuracy as our best. Though all these models we’ve seen are performing comparably.
show_best(fits_ngram)Warning in show_best(fits_ngram): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 9
penalty mixture max_tokens .metric .estimator mean n std_err
<dbl> <dbl> <int> <chr> <chr> <dbl> <int> <dbl>
1 0.00546 0.8 5000 bal_accuracy binary 0.726 1 NA
2 0.00546 1 5000 bal_accuracy binary 0.725 1 NA
3 0.0183 0.4 4000 bal_accuracy binary 0.724 1 NA
4 0.00546 1 4000 bal_accuracy binary 0.724 1 NA
5 0.0183 0.6 5000 bal_accuracy binary 0.720 1 NA
.config
<chr>
1 Preprocessor2_Model090
2 Preprocessor2_Model110
3 Preprocessor1_Model051
4 Preprocessor1_Model110
5 Preprocessor2_Model071Here is the plot of accuracy against our hyperparameters.
autoplot(fits_ngram)
Let’s run these last configurations, but including stopwords and see if that gives use anything.
rec_ngram_stop <- recipe(user_drinking ~ text, data = data_train_clean) |>
step_string2factor(user_drinking) |>
step_tokenize(text, engine = "tokenizers", token = "ngrams",
options = list(n = 2, n_min = 1, stopwords = stops)) |>
step_tokenfilter(text, max_tokens = tune::tune()) |>
step_tfidf(text) |>
step_normalize(all_predictors())
fits_ngram_stop <-
logistic_reg(penalty = tune::tune(),
mixture = tune::tune()) |>
set_engine("glmnet") |>
tune_grid(preprocessor = rec_ngram,
resamples = splits,
grid = grid_tokens,
metrics = metric_set(bal_accuracy))We are still right around 72-73%. All these differences in performance are nominal.
show_best(fits_ngram_stop)Warning in show_best(fits_ngram_stop): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 9
penalty mixture max_tokens .metric .estimator mean n std_err
<dbl> <dbl> <int> <chr> <chr> <dbl> <int> <dbl>
1 0.00546 0.8 5000 bal_accuracy binary 0.726 1 NA
2 0.00546 1 5000 bal_accuracy binary 0.725 1 NA
3 0.0183 0.4 4000 bal_accuracy binary 0.724 1 NA
4 0.00546 1 4000 bal_accuracy binary 0.724 1 NA
5 0.0183 0.6 5000 bal_accuracy binary 0.720 1 NA
.config
<chr>
1 Preprocessor2_Model090
2 Preprocessor2_Model110
3 Preprocessor1_Model051
4 Preprocessor1_Model110
5 Preprocessor2_Model071Still getting clear winners over our hyperparameter values.
autoplot(fits_ngram_stop)
For the last models, let’s see if we can get any better performance by adding the GloVe embeddings. Here the grid won’t include the max_tokens.
grid_glove <- grid_regular(penalty(range = c(-7, 3)),
mixture(),
levels = c(20, 6))
rec_glove <- recipe(user_drinking ~ text, data = data_train_clean) |>
step_string2factor(user_drinking) |>
step_tokenize(text,
engine = "tokenizers", token = "words",
options = list(stopwords =stop_smart)) |>
step_word_embeddings(text, embeddings = glove_embeddings)
fits_glove <-
logistic_reg(penalty = tune::tune(),
mixture = tune::tune()) |>
set_engine("glmnet") |>
tune_grid(preprocessor = rec_glove,
resamples = splits,
grid = grid_glove,
metrics = metric_set(bal_accuracy))We are still stuck right around 73% within the right range in our hyperparameter landscape.
show_best(fits_glove)Warning in show_best(fits_glove): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 8
penalty mixture .metric .estimator mean n std_err
<dbl> <dbl> <chr> <chr> <dbl> <int> <dbl>
1 0.0183 0.4 bal_accuracy binary 0.511 1 NA
2 0.0183 0.2 bal_accuracy binary 0.510 1 NA
3 0.00546 1 bal_accuracy binary 0.510 1 NA
4 0.207 0 bal_accuracy binary 0.510 1 NA
5 0.00546 0.6 bal_accuracy binary 0.509 1 NA
.config
<chr>
1 Preprocessor1_Model051
2 Preprocessor1_Model031
3 Preprocessor1_Model110
4 Preprocessor1_Model013
5 Preprocessor1_Model070Hyperparameters
autoplot(fits_ngram_stop)
We settle for our first configuration with unigrams and no stopwords. There wasn’t a lot of variability in performance across all these configurations, so in order to push our efforts further here we would likely need to return to cleaning and/or consider different possible architectures to enhance our predictive capacity. For now, let’s select our best, retrain on all our data, and predict into test.
Part 3: Best Model
Since we didn’t hold out an independent test set, instead we selected our model configuration based on cross validated performance. That means that our selection of a best model here is subject to some degree of optimization bias.
show_best(fits_unigram)Warning in show_best(fits_unigram): No value of `metric` was given;
"bal_accuracy" will be used.# A tibble: 5 × 9
penalty mixture max_tokens .metric .estimator mean n std_err
<dbl> <dbl> <int> <chr> <chr> <dbl> <int> <dbl>
1 0.00546 1 4000 bal_accuracy binary 0.735 1 NA
2 0.00546 1 5000 bal_accuracy binary 0.730 1 NA
3 0.00546 0.8 4000 bal_accuracy binary 0.730 1 NA
4 0.00546 0.8 5000 bal_accuracy binary 0.727 1 NA
5 0.0183 0.4 5000 bal_accuracy binary 0.726 1 NA
.config
<chr>
1 Preprocessor1_Model110
2 Preprocessor2_Model110
3 Preprocessor1_Model090
4 Preprocessor2_Model090
5 Preprocessor2_Model051Train your top performing model on the full data set
rec_uni_final = recipe(user_drinking ~ text, data = cleaned_data) |>
step_string2factor(user_drinking) |>
step_tokenize(text, engine = "tokenizers", token = "words") |>
step_tokenfilter(text, max_tokens = 4000) |>
step_tfidf(text)
feat_final <- rec_uni_final |>
prep(cleaned_data) |>
bake(NULL)
# Final unigram
final_model <-
logistic_reg(penalty = select_best(fits_unigram)$penalty,
mixture = select_best(fits_unigram)$mixture) |>
set_engine("glmnet") |>
fit(user_drinking ~ ., data = feat_final)Warning in select_best(fits_unigram): No value of `metric` was given; "bal_accuracy" will be used.
No value of `metric` was given; "bal_accuracy" will be used.
No value of `metric` was given; "bal_accuracy" will be used.
No value of `metric` was given; "bal_accuracy" will be used.
No value of `metric` was given; "bal_accuracy" will be used.Note: Here we get accuracy of the full model as a demonstration of a biased metric. We did not use held test out data, instead we used a validation split, so we cannot get a proper estimate of accuracy with the full model (we already have an estimate of accuracy from earlier that is a better estimate of how our model would do with new data, even though it still has some optimization bias).
accuracy_vec(feat_final$user_drinking, predict(final_model, feat_final)$.pred_class)Warning in select_best(fits_unigram): No value of `metric` was given;
"bal_accuracy" will be used.[1] 0.9058308Variable importance
Note: So far, you have learned about model agnostic approaches for feature importance (meaning that you can use them for any model). This approach is model specific and can be used for GLMs or models with interpretable and standardized features (not random forest or knn for example)
Let’s plot variable importance scores of the final model. As is typical with text data there are a lot of words here that are difficult to explain (some aren’t “words” at all). However, we do see some obvious predictors. Unsurprisingly, the top predictor in the positive class is “drinking”. Some more interesting examples from that level of the outcome are “address”, “beginning”, “comfy”, and “cope”. From the negative class it is more difficult to interpret the particular terms. However, I would note that many of these are usernames. This makes sense given that we include a tfidf computation in our recipe. The tfidf step computes a frequency that adjusts for the fact that many words are just in general more frequent. The result of this is that you have words that are very frequent in particular documents without being frequent in general. The names of specific users and stylized words talking about the content covered in these tweets are likely to pop out of such a statistic, which is basically what we see. However, we’d want to consider in future models whether or not we remove usernames. In a sense, this isn’t helpful for prediction; it is basically revealing that some users are un/likely to be tweeting about drinking.
final_model$fit |>
vip::vi(method = "model") |>
group_by(Sign) |>
top_n(30, wt = abs(Importance)) |>
ungroup() |>
mutate(
Importance = abs(Importance),
Variable = str_remove(Variable, "tfidf_text_"),
Variable = fct_reorder(Variable, Importance)
) |>
ggplot(aes(x = Importance, y = Variable, fill = Sign)) +
geom_col(show.legend = FALSE) +
facet_wrap(~Sign, scales = "free_y") +
labs(y = NULL)
# you can also access the variables directly, here by their importance
final_model$fit |>
vip::vi() |>
group_by(Sign) |>
top_n(30, wt = abs(Importance)) |>
kbl() |> kable_styling(bootstrap_options = c('striped', 'condensed')) |>
scroll_box(height = "500px", width = "100%")Warning in attr(x, "align"): 'xfun::attr()' is deprecated.
Use 'xfun::attr2()' instead.
See help("Deprecated")| Variable | Importance | Sign |
|---|---|---|
| tfidf_text_drinking | 21.460174 | POS |
| tfidf_text_alyacx0 | 20.516106 | POS |
| tfidf_text_burning | 18.639956 | POS |
| tfidf_text_address | 17.631899 | POS |
| tfidf_text_daveweigel | 17.533295 | NEG |
| tfidf_text_beginning | 17.257474 | POS |
| tfidf_text_comfy | 16.980029 | POS |
| tfidf_text_appreciate | 16.806356 | POS |
| tfidf_text_alcoholic2nd | 15.828003 | POS |
| tfidf_text_anna | 15.800637 | POS |
| tfidf_text_constant | 15.418527 | POS |
| tfidf_text_cupcake | 14.679457 | NEG |
| tfidf_text_2cw | 14.568564 | NEG |
| tfidf_text_bedtime | 14.179910 | POS |
| tfidf_text_hardcore | 13.914527 | POS |
| tfidf_text_alb | 13.724453 | POS |
| tfidf_text_atlantic | 13.648685 | NEG |
| tfidf_text_lagunitasbruhws | 13.605240 | NEG |
| tfidf_text_wine | 13.357435 | POS |
| tfidf_text_risk | 13.326721 | NEG |
| tfidf_text_crush | 13.217909 | POS |
| tfidf_text_club | 13.174448 | NEG |
| tfidf_text_cube | 12.917229 | NEG |
| tfidf_text_bump | 12.916775 | POS |
| tfidf_text_shot | 12.901388 | NEG |
| tfidf_text_erie | 12.717926 | POS |
| tfidf_text_cope | 12.647198 | POS |
| tfidf_text_aw | 12.626846 | POS |
| tfidf_text_boring | 12.566367 | NEG |
| tfidf_text_bigger | 12.561789 | POS |
| tfidf_text_exit33ts | 12.541591 | POS |
| tfidf_text_dearannabler | 12.445207 | POS |
| tfidf_text_drink | 12.339448 | POS |
| tfidf_text_630 | 12.151966 | POS |
| tfidf_text_blood | 12.069611 | POS |
| tfidf_text_random | 11.952779 | POS |
| tfidf_text_dong | 11.925402 | NEG |
| tfidf_text_3am | 11.921090 | NEG |
| tfidf_text_official | 11.466524 | POS |
| tfidf_text_brazil | 11.393160 | NEG |
| tfidf_text_afrorepublican | 11.135434 | NEG |
| tfidf_text_sick | 11.059699 | NEG |
| tfidf_text_lastnight | 11.039563 | POS |
| tfidf_text_bought | 10.849869 | POS |
| tfidf_text_designed | 10.796892 | NEG |
| tfidf_text_radio | 10.744086 | NEG |
| tfidf_text_beardedbobo | 10.651113 | NEG |
| tfidf_text_thinkin | 10.632487 | NEG |
| tfidf_text_beer | 10.625522 | POS |
| tfidf_text_amateur | 10.624771 | NEG |
| tfidf_text_maxyice817 | 10.446948 | NEG |
| tfidf_text_brain | 10.275495 | NEG |
| tfidf_text_considered | 9.428120 | NEG |
| tfidf_text_2011 | 9.395636 | NEG |
| tfidf_text_youll | 9.245398 | NEG |
| tfidf_text_damnberrr | 9.173351 | NEG |
| tfidf_text_barn | 9.080704 | NEG |
| tfidf_text_scream | 8.758610 | NEG |
| tfidf_text_chalotte | 8.693713 | NEG |
| tfidf_text_burgerking | 8.683799 | NEG |
Extra: considering the timestamp of tweets
Working on Twitter data made us wonder about other information we could get from the tweet id. This was not part of the assignment, but we wanted to share in case any of you are interested in Twitter data (credit to the amazing Sarah Sant’Ana for the work on this):
Here’s some inside information on Tweets and how they are identified in the computing systems that distribute the social network we all use. You weren’t expected to know any of this - this is new information for you (that will hopefully help your future work in data science and research if you use NLP-based methods).
Tweet ids are a unique form of identifier called a snowflake. These are based on the millisecond of time during which they were created. They are stored in bits 22 - 64 of the ID string. Additional bits code for machine specific information (like worker, data center, sequence IDs bits of different machines) that allow for multiple tweets to have unique IDs despite being created within the same millisecond. Here is a function that uses pre-defined bit lengths stated by Twitter to extract the timestamp from tweets. It returns a date object known as a “POSIX”, which is a fancy type of data in R that codes human-readable dates and times.
tweet_timestamp <-function(tweet_id){
library(bit64)
library(lubridate)
tweet_id <- as.integer64(tweet_id) # ID is 64-bit
tweet_epoch <- as.integer64('1288834974657') # Twitter epoch constant
# Shift all non-timestamp bits out of the variable
timestamp_ms <- tweet_id %/%
as.integer64(4096) %/% # number of bits allocated for worker (5)
as.integer64(32) %/% # data center (5)
as.integer64(32) # sequence ID bits
# Note: %/% is the integer division operator.
# Add on the twitter epoch constant
timestamp_ms <- timestamp_ms + tweet_epoch
timestamp_ms <- milliseconds(timestamp_ms)
return(as_date(timestamp_ms))
}Our hope is to use this to examine outcomes and other variables of interest in these tweet data, so we thought we would pass it along given that tweets are a super useful and available source of language and other social information for data scientific and research applications.