rake-nltk VS flashtext

Compare rake-nltk vs flashtext and see what are their differences.

rake-nltk

Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK. (by csurfer)

flashtext

Extract Keywords from sentence or Replace keywords in sentences. (by vi3k6i5)
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rake-nltk flashtext
4 8
1,060 5,596
- -
0.0 0.0
almost 2 years ago 4 months ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

rake-nltk

Posts with mentions or reviews of rake-nltk. We have used some of these posts to build our list of alternatives and similar projects.

flashtext

Posts with mentions or reviews of flashtext. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-14.

What are some alternatives?

When comparing rake-nltk and flashtext you can also consider the following projects:

yake - Single-document unsupervised keyword extraction

KeyBERT - Minimal keyword extraction with BERT

pke - Python Keyphrase Extraction module

magnitude - A fast, efficient universal vector embedding utility package.

NLTK - NLTK Source

Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark

WordDumb - A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.

simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.

gensim - Topic Modelling for Humans

hepscrape - arXiv:hep-ph scraper

AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity

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