Distributions¶

`freq_dist`(tokens)	Count frequency of tokens
`compute_tfidf`(text_list[, doc_ids])	Compute tfidf
`important_words_per_corpus`(scores[, n])	Based on tfidf scores, return most important words per corpus
`important_words_per_doc`(scores[, doc_id, n])	Based on tfidf scores, return most important words per document

Linguistics¶

pos_tag(model, text)

Return parts-of-speech for words in a peice of text.

`word_vectors`(docs[, sg, mc, sz, wnd, epochs])	Compute word vectors from corpus
`similar_words`(wvs, word[, n])	Find similar words to word
`vector_maths`(wvs[, pwords, nwords, n])	Perform word vector maths
`save_vectors`(wvs, fname[, binary])	Save word vector model to file
`load_vectors`(fname[, binary])	Load word vector model from file

`bow_topic_modelling`(docs[, no_topics])	LDA Topic Modelling with BoW
`tfidf_topic_modelling`(docs[, no_topics])	LDA Topic Modelling with TF-IDF
`print_topic_words`(topic_model)	Print words corresponding to topic modelling
`determine_topics`(doc, topic_model, dictionary)	determine document topics

Compute sentiment strength of ENGLISH texts

keyphrase_list(text[, language, stopwords, …])

Extract keywords from a piece of text