{
    "info": {
        "author": "Anurag Kumar",
        "author_email": "anuragkumarak95@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python",
            "Topic :: Utilities"
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        "description": "# WordNet\n\n[![Build Status](https://travis-ci.org/anuragkumarak95/wordnet.svg?branch=master)](https://travis-ci.org/anuragkumarak95/wordnet)\n[![codecov](https://codecov.io/gh/anuragkumarak95/wordnet/branch/master/graph/badge.svg)](https://codecov.io/gh/anuragkumarak95/wordnet)\n[![Requirements Status](https://requires.io/github/anuragkumarak95/wordnet/requirements.svg?branch=master)](https://requires.io/github/anuragkumarak95/wordnet/requirements/?branch=master)\n\nCreate a Simple **network of words** related to each other using **Twitter Streaming API**.\n\n![Made with python-3.5](http://forthebadge.com/images/badges/made-with-python.svg)\n\nMajor parts of this project.\n\n* `Streamer` : ~/twitter_streaming.py\n* `TF-IDF` Gene : ~/wordnet/tf_idf_generator.py\n* `NN` words Gene :~/ wordnet/nn_words.py\n* `NETWORK` Gene : ~/wordnet/word_net.py\n\n## Using Streamer Functionality\n\n1. `Clone this repo` and run on bash '`$pip install -r requirements.txt`' @ root directory and you will be ready to go..\n\n1. Go to root-dir(~), Create a config.py file with details mentioned below:\n    ```python\n    # Variables that contains the user credentials to access Twitter Streaming API\n    # this link will help you(http://socialmedia-class.org/twittertutorial.html)\n    access_token = \"xxx-xx-xxxx\"\n    access_token_secret = \"xxxxx\"\n    consumer_key = \"xxxxxx\"\n    consumer_secret = \"xxxxxxxx\"\n    ```\n1. run `Streamer` with an array of filter words that you want to fetch tweets on. eg. `$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`.\n\n## Using WordNet Module\n\n1. `Clone this repo` and install wordnet module using this script,\n\n        $python setup.py install\n\n1. To create a `TF-IDF` structure file for every doc, use:\n\n    ```python\n    from wordnet import find_tf_idf\n\n    df, tf_idf = find_tf_idf(\n    file_names=['file/path1','file/path2',..],       # paths of files to be processed.(create using twitter_streamer.py)\n    prev_file_path='prev/tf/idf/file/path.tfidfpkl', # prev TF_IDF file to modify over, format standard is .tfidfpkl. default = None\n    dump_path='path/to/dump/file.tfidfpkl'           # dump_path if tf-idf needs to be dumped, format standard is .tfidfpkl. default = None\n    )\n\n    '''\n    if no file is provided prev_file_path parameter, new TF-IDF file will be generated ,and else\n    TF-IDF values will be combined with previous file, and dumped at dump_path if mentioned,\n    else will only return the new tf-idf list of dictionaries, and df dictionary.\n    '''\n    ```\n1. To use `NN` Word Gene of this module, simply use wordnet.find_knn:\n\n    ```python\n    from wordnet import find_knn\n\n    words = find_knn(\n    tf_idf=tf_idf,       # this tf_idf is returned by find_tf_idf() above.\n    input_word='german', # a word for which k nearest neighbours are required.\n    k=10,                # k = number of neighbours required, default=10\n    rand_on=True         # rand_on = either to randomly skip few words or show initial k words default=True\n    )\n\n    '''\n    This function will return a list of words closely related to provided input_word refering to\n    tf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dump\n    file dumped by the same function at your choosen directory. the file contains 2 lists in format\n    (idf, tf_idf).\n    '''\n    ```\n\n1. To create a Word `Network`, use :\n\n    ```python\n    from wordnet import generate_net\n\n    word_net = generate_net(\n    df=df,                          # this df is returned by find_tf_idf() above.\n    tf_idf=tf_idf,                  # this tf_idf is returned by find_tf_idf() above.\n    dump_path='path/to/dump.wrnt'   # dump_path = path to dump the generated files, format standard is .wrnt. default=None\n    )\n\n    '''\n    this function returns a dict of Word entities, with word as key.\n    '''\n    ```\n\n1. To retrieve a Word `Network`, use :\n\n    ```python\n    from wordnet import retrieve_net\n\n    word_net = retrieve_net(\n        'path/to/network.wrnt' # path to network file, format standard is .wrnt.\n        )\n    '''\n    this function returns a dictionary of Word entities, with word as key.\n    '''\n    ```\n\n1. To retrieve list of words that are at some depth form a root word in the network, use:\n\n    ```python\n    from wordnet import return_net\n\n    words = return_net(\n        word,       # root word in this process.\n        word_net,   # word network generated from generate_net()\n        depth=1    # depth to which you wish this word collector to traverse.\n    )\n    '''\n    This function returns a list of words that are at provided depth from root word in the\n    network provided.\n    '''\n    ```\n\n### Test Run\n\nTo run a formal test, simply run this script. `python test.py`, this module will return **0** if everythinig worked as expected.\n\ntest.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()`.\n\n> `Streamer` functionality will not be provided under distribution of this code. That is just a script independent from the module.\n\n#### Contributions Are welcomed here\n\n![BUILT WITH LOVE](http://forthebadge.com/images/badges/built-with-love.svg)\n\nby [@Anurag](https://github.com/anuragkumarak95)\n",
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        "summary": "An module to create network of words on bases of realtive sense under a corpus of document.",
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