topicextractor 0.0.0

Extracts keywords with 'TF-IDF' algorithm

"topicextractor" extracts topic keywords from documents based on 'TF-IDF' algorithm.

Usage

import topicextractor as te

sample_docs = [["doc1_str"], ["doc2_str"], ["doc3_str"] ....]

#extract noun counts from 'sample_docs' (yon can omit this step and preprocess the data in your own way)

count_container = te.extract_noun_counts(sample_docs)

print(count_container) [{"a": 3, "b": 2}, {"c": 5, "d":3}, {"e": 7, "f": 12} ....]

#extract keywords for 'count_container[0]'(=the first docmunet in 'sample_docs')

keywords = te.tfidf(count_container[0], count_container)

print(keywords) [("keyword1", 2.1104),("keyword2" 2.0012),("keyword3", 1.8892) ....]

Thanks, and please contact the author via e-mail for any comment.