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politicaltweets: Classify political tweets

The politicaltweets R package provides functions to preprocess and classify tweets data according to whether or not they are political based on a pre-trained ensemble classifier.

Installation

remotes::install_github("haukelicht/politicaltweets")

Note that all but one package dependencies are distributed via CRAN. The one exeption is the laserize package, which can be installed from GitHub.

Usage

To classify a tweet, five steps are required

  1. Query the tweet data from the Twitter API using rtweet's lookup_statuses() (with parse = TRUE).
  2. pass the parsed tweets data to argument x of create_tweet_features() to create data frame of tweet features
  3. pass the parsed tweets data to argument x of create_tweet_text_representations() with .compute.pcs = FALSE to create obtain tweet text embeding representations1
  4. combine the tweet features and text representation objects in a data frame.
  5. pass the resulting data frame to argument x of classify_tweets()

A minimal workin example:

library(dplyr)
library(politicaltweets)

# instead of querying data from the Tweet API (step 1)
# below we use a prototypical tweets data frame
glimpse(tweets.df.prototype)

# step 2
tfeats <- create_tweet_features(tweets.df.prototype, .as.data.table = FALSE)

# step 3
ttreps <- create_tweet_text_representations(tweets.df.prototype, .compute.pcs = FALSE)

# step 4
temp <- as_tibble(tfeats) %>%
  left_join(mutate(as_tibble(ttreps$ics), status_id = rownames(ttreps$ics)))
  
# step 5
preds <- classify_tweets(temp, .debug = TRUE) 

# inspect the result
cbind(temp[, c("text", "lang")], preds)

Details

Required format of x

All functions exported by politicaltweets expect that data passed to their arguments x conforms the naming and typing conventions of tweets data frames set by the rtweet package.

A prototypical tweets data frame is distributed with the politicaltweets package, see ?tweets.df.prototype. (Moreover, politicaltweets::required.tweets.df.cols maps required columns to the accepted classes.)

Using classify_tweets() with a pre-trained ensemble classifier

classify_tweets() can handle two types of model input:

By default, classify_tweets() uses a list of four pre-trained models (see ?constituent.modles for details) "blends" them into an ensemble classifier using blend.by = "PR-AUC" (maximize the area under the precision-recall curve).

More generally, classify_tweets() can handle two types of model inputs:

  1. Lists of pre-trained base learner models: if the input to argument model is a 'caretList' object (i.e., a list of pre-trained base learners). In this case, the base learners are first "blended" into a greedy ensemble classifier, and the resulting ensemble model is then used to classify samples in x.
  2. Pre-trained ensemble classifiers: If the input to argument model is a 'caretEnsemble' object, this ensemble model is directly used to classify samples in x.

Thus you can train o

Footnotes

  1. It obtains tweet text LASER embedding representations using the laserize package and projects tweets LASER representations onto a pre-defined independent component space. ↩