"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' 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.
FAQs
Extracts keywords with 'TF-IDF' algorithm
We found that topicextractor demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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