rake-nltk
hepscrape
| rake-nltk | hepscrape | |
|---|---|---|
| 4 | 2 | |
| 1,060 | 0 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | almost 3 years ago | |
| Python | R | |
| MIT License | GNU General Public License v3.0 only |
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rake-nltk
- rake-nltk 1.0.6 released. Comes with the flexibility to choose your own sentence and word tokenizers.
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PMI for WordClouds
I'm not sure what you mean by tokenizing phrases or concepts. Specifically extracting institution names would fall under NER. You can do this with spaCy. Extracting commonly used phrases would fall under keyword extraction. For this, you can study frequencies of n-grams of length > 1 and optionally filter based on POS (i.e. NOUN+ADJ). I've never used RAKE (https://github.com/csurfer/rake-nltk) but I've heard this is also a popular method.
hepscrape
What are some alternatives?
yake - Single-document unsupervised keyword extraction
textfeatures - 👷♂️ A simple package for extracting useful features from character objects 👷♀️
pke - Python Keyphrase Extraction module
quanteda - An R package for the Quantitative Analysis of Textual Data
NLTK - NLTK Source
tidyethnicnews - R package for turning Ethnic NewsWatch search results into tidyverse-ready dataframes
flashtext - Extract Keywords from sentence or Replace keywords in sentences.
tidytext - Text mining using tidy tools :sparkles::page_facing_up::sparkles:
WordDumb - A calibre plugin that generates Kindle Word Wise and X-Ray files for KFX, AZW3, MOBI and EPUB eBook.
awesome-computational-social-science - A list of awesome resources for Computational Social Science
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
DataScienceR - a curated list of R tutorials for Data Science, NLP and Machine Learning