Package: wordnet
# 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 Functionality1. `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 Module1. `Clone this repo` and install wordnet module using this script,$python setup.py install1. To create a `TF-IDF` structure file for every doc, use:```pythonfrom wordnet import find_tf_idfdf, 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 = Nonedump_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 elseTF-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:```pythonfrom wordnet import find_knnwords = 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=10rand_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 totf_idf var provided to it. either use find_tf_idf() to gather this var or pickle.load() a dumpfile 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 :```pythonfrom wordnet import generate_netword_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 :```pythonfrom wordnet import retrieve_netword_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:```pythonfrom wordnet import return_networds = 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 thenetwork provided.'''```### Test RunTo 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)