In koheiw/quanteda.core: Quantitative Analysis of Textual Data

knitr::opts_chunk$set(
  collapse = FALSE,
  comment = "##"
)

Using quanteda's fcm() and textplot_network(), you can perform visual analysis of social media posts in terms of cooccurances of hashtags or usernames in a few steps. The dataset for this example contains only 10,000 Twitter posts, but you can easily analyze more one million posts on your laptop computer.

library(quanteda)

Load sample data

load("data/data_corpus_tweets.rda")

Construct a document-feature matrix of Twitter posts

tweet_dfm <- dfm(data_corpus_tweets, remove_punct = TRUE)
head(tweet_dfm)

Hashtags

Extract most common hashtags

tag_dfm <- dfm_select(tweet_dfm, ('#*'))
toptag <- names(topfeatures(tag_dfm, 50))
head(toptag)

Construct feature-occurrence matrix of hashtags

tag_fcm <- fcm(tag_dfm)
head(tag_fcm)
topgat_fcm <- fcm_select(tag_fcm, toptag)
textplot_network(topgat_fcm, min_freq = 0.1, edge_alpha = 0.8, edge_size = 5)

Usernames

Extract most frequently mentioned usernames

user_dfm <- dfm_select(tweet_dfm, ('@*'))
topuser <- names(topfeatures(user_dfm, 50))
head(topuser)

Construct feature-occurrence matrix of usernames

user_fcm <- fcm(user_dfm)
head(user_fcm)
user_fcm <- fcm_select(user_fcm, topuser)
textplot_network(user_fcm, min_freq = 0.1, edge_color = 'orange', edge_alpha = 0.8, edge_size = 5)