Metadata-Version: 2.1
Name: feature_mining
Version: 0.1.1
Summary: Mine implicit features using a generative feature language model.
Home-page: https://github.com/pypa/sampleproject
Author: H. Wilder, N. Freundlich, Santu Karmaker
Author-email: hwilder3@illinois.edu, norbert4@illinois.edu, karmake2@illinois.edu
License: UNKNOWN
Description: # 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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
