All functions

calc_accuracy_per_class()

Calculate classifier accuracy for each class and group

calc_bigrams_network()

Create and count bigrams

calc_bing_word_counts()

Counts of words with a positive or negative sentiment

calc_confusion_matrix()

Calculate the confusion matrix

calc_net_sentiment_nrc()

Calculate "net sentiment" in a text

calc_net_sentiment_per_tag()

Calculate "net positive" and "net negative" sentiment in a text

calc_tfidf_ngrams()

Calculate TF-IDFs for unigrams or bigrams

get_dictionary()

Check for sentiment dictionaries

plot_bigrams_network()

Plot a network of bigrams

plot_bing_word_counts()

Plot bar plots of the most frequent words.

plot_confusion_matrix()

Plot a confusion matrix

plot_net_sentiment_long_nrc()

Plot sentiment counts in a text

plot_net_sentiment_per_tag()

Plot "net sentiment" in a text

plot_tfidf_ngrams()

Plot the n-grams with the highest TF-IDFs

prep_sentiments_nrc()

Pulls NRC Sentiments

prep_tidy_text()

Unnest tokens for each label in a labelled text

tidy_filter_null()

Filter data frame when filter can be NULL

tidy_net_sentiment_nrc()

Order sentiment occurrence table by sentiment counts