Metadata-Version: 1.1
Name: wordnet
Version: 0.0.1b2
Summary: An module to create network of words on bases of realtive sense under a corpus of document.
Home-page: https://anuragkumarak95.github.io/wordnet/
Author: Anurag Kumar
Author-email: anuragkumarak95@gmail.com
License: GNU General Public License v3
Description-Content-Type: UNKNOWN
Description: # WordNet
        
        [![Build Status](https://travis-ci.org/anuragkumarak95/wordnet.svg?branch=master)](https://travis-ci.org/anuragkumarak95/wordnet)
        [![codecov](https://codecov.io/gh/anuragkumarak95/wordnet/branch/master/graph/badge.svg)](https://codecov.io/gh/anuragkumarak95/wordnet)
        [![Requirements Status](https://requires.io/github/anuragkumarak95/wordnet/requirements.svg?branch=master)](https://requires.io/github/anuragkumarak95/wordnet/requirements/?branch=master)
        
        Create a Simple **network of words** related to each other using **Twitter Streaming API**.
        
        ![Made with python-3.5](http://forthebadge.com/images/badges/made-with-python.svg)
        
        Major parts of this project.
        
        * `Streamer` : ~/twitter_streaming.py
        * `TF-IDF` Gene : ~/wordnet/tf_idf_generator.py
        * `NN` words Gene :~/ wordnet/nn_words.py
        * `NETWORK` Gene : ~/wordnet/word_net.py
        
        ## Using Streamer Functionality
        
        1. `Clone this repo` and run on bash '`$pip install -r requirements.txt`' @ root directory and you will be ready to go..
        
        1. Go to root-dir(~), Create a config.py file with details mentioned below:
            ```python
            # Variables that contains the user credentials to access Twitter Streaming API
            # this link will help you(http://socialmedia-class.org/twittertutorial.html)
            access_token = "xxx-xx-xxxx"
            access_token_secret = "xxxxx"
            consumer_key = "xxxxxx"
            consumer_secret = "xxxxxxxx"
            ```
        1. run `Streamer` with an array of filter words that you want to fetch tweets on. eg. `$python twitter_streaming.py hello hi hallo namaste > data_file.txt` this will save a line by line words from tweets filtered according to words used as args in `data_file.txt`.
        
        ## Using WordNet Module
        
        1. `Clone this repo` and install wordnet module using this script,
        
                $python setup.py install
        
        1. To create a `TF-IDF` structure file for every doc, use:
        
            ```python
            from wordnet import find_tf_idf
        
            df, tf_idf = find_tf_idf(
            file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)
            prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None
            dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None
            )
        
            '''
            if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else
            TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,
            else will only return the new tf-idf list of dictionaries, and df dictionary.
            '''
            ```
        1. To use `NN` Word Gene of this module, simply use wordnet.find_knn:
        
            ```python
            from wordnet import find_knn
        
            words = find_knn(
            tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.
            input_word='german', # a word for which k nearest neighbours are required.
            k=10,                # k = number of neighbours required, default=10
            rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True
            )
        
            '''
            This function will return a list of words closely related to provided input_word refering to
            tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump
            file dumped by the same function at your choosen directory. the file contains 2 lists in format
            (idf, tf_idf).
            '''
            ```
        
        1. To create a Word `Network`, use :
        
            ```python
            from wordnet import generate_net
        
            word_net = generate_net(
            df=df,                          # this df is returned by find_tf_idf() above.
            tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.
            dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None
            )
        
            '''
            this function returns a dict of Word entities, with word as key.
            '''
            ```
        
        1. To retrieve a Word `Network`, use :
        
            ```python
            from wordnet import retrieve_net
        
            word_net = retrieve_net(
                'path/to/network.wrnt' # path to network file, format standard is .wrnt.
                )
            '''
            this function returns a dictionary of Word entities, with word as key.
            '''
            ```
        
        1. To retrieve list of words that are at some depth form a root word in the network, use:
        
            ```python
            from wordnet import return_net
        
            words = return_net(
                word,       # root word in this process.
                word_net,   # word network generated from generate_net()
                depth=1    # depth to which you wish this word collector to traverse.
            )
            '''
            This function returns a list of words that are at provided depth from root word in the
            network provided.
            '''
            ```
        
        ### Test Run
        
        To run a formal test, simply run this script. `python test.py`, this module will return **0** if everythinig worked as expected.
        
        test.py uses sample data provided [here](https://github.com/anuragkumarak95/wordnet/tree/master/test) and executes unittest on `find_tf_idf()`, `find_knn()` & `generate_net()`.
        
        > `Streamer` functionality will not be provided under distribution of this code. That is just a script independent from the module.
        
        #### Contributions Are welcomed here
        
        ![BUILT WITH LOVE](http://forthebadge.com/images/badges/built-with-love.svg)
        
        by [@Anurag](https://github.com/anuragkumarak95)
        
Keywords: word network python twitter streaming data
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
