Man pages for CDU-data-science-team/experienceAnalysis
Helper Functions for Text Mining
| 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... |
| calc_tfidf_ngrams | Calculate TF-IDFs for unigrams or bigrams |
| get_dictionary | Check for sentiment dictionaries |
| pipe | Pipe operator |
| 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 |
