GFLM: mine implicit features using a generative feature language model
Description
This package implements a Generative Feature Language Models for Mining Implicit Features.
Given the following input:
- a text dataset
- a set of predefined features
Compute the following:
- mapping of explicit and implicit features on the data
- using both gflm_word and gflm_section algorithms
Install
pip install feature_mining
Sample Usage
Usage:
from feature_mining import FeatureMining
fm = FeatureMining()
fm.load_ipod(full_set=False)
fm.fit()
fm.predict()
Results:
- prediction using 'section': fm.gflm.gflm_section
- prediction using 'word': fm.gflm.gflm_word
Display result:
fm.section_features()
print(fm.gflm_section_result.sort_values(by=['gflm_section'], ascending=False)[['feature', 'section_text']].head(20))
Package created based on the following paper
S. Karmaker Santu, P. Sondhi and C. Zhai, "Generative Feature Language Models for Mining Implicit Features from Customer Reviews", Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16, 2016.
Pydocs (Code Documentation)
Accessible via this link: http://htmlpreview.github.io/?https://github.com/nfreundlich/CS410_CourseProject/blob/dev/docs/feature_mining.html
(Apologies for the color scheme - it was the default)
Tutorial
See Jupyter notebook tutorial https://github.com/nfreundlich/CS410_CourseProject/blob/dev/tutorial.ipynb
Video presentation and tutorial
Link to YouTube: https://www.youtube.com/watch?v=mjJHkyrkxHM
Package on PyPi
https://pypi.org/project/feature-mining/
Slides
https://github.com/nfreundlich/CS410_CourseProject/blob/dev/docs/CS_410_GFLM_Slides.pdf
Known Issues
Explicit feature mentions not removed from GFLM word/sentence: https://github.com/nfreundlich/CS410_CourseProject/issues/28
Release files for feature-mining 0.1.1
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Total release size: 878.1 kB
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