calc_accuracy_per_class()
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Calculate classifier accuracy for each class and group |
calc_bigrams_network()
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Create and count bigrams |
calc_bing_word_counts()
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Counts of words with a positive or negative sentiment |
calc_confusion_matrix()
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Calculate the confusion matrix |
calc_net_sentiment_nrc()
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Calculate "net sentiment" in a text |
calc_net_sentiment_per_tag()
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Calculate "net positive" and "net negative" sentiment in a text |
calc_tfidf_ngrams()
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Calculate TF-IDFs for unigrams or bigrams |
get_dictionary()
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Check for sentiment dictionaries |
plot_bigrams_network()
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Plot a network of bigrams |
plot_bing_word_counts()
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Plot bar plots of the most frequent words. |
plot_confusion_matrix()
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Plot a confusion matrix |
plot_net_sentiment_long_nrc()
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Plot sentiment counts in a text |
plot_net_sentiment_per_tag()
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Plot "net sentiment" in a text |
plot_tfidf_ngrams()
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Plot the n-grams with the highest TF-IDFs |
prep_sentiments_nrc()
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Pulls NRC Sentiments |
prep_tidy_text()
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Unnest tokens for each label in a labelled text |
tidy_filter_null()
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Filter data frame when filter can be NULL |
tidy_net_sentiment_nrc()
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Order sentiment occurrence table by sentiment counts |