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An module to create network of words on bases of realtive sense under a corpus of document.

Project description

# WordNet

[![Build Status](](
[![Requirements Status](](

Create a Simple **network of words** related to each other using **Twitter Streaming API**.

![Made with python-3.5](

Major parts of this project.

* `Streamer` : ~/
* `TF-IDF` Gene : ~/wordnet/
* `NN` words Gene :~/ wordnet/
* `NETWORK` Gene : ~/wordnet/

## 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 file with details mentioned below:
# Variables that contains the user credentials to access Twitter Streaming API
# this link will help you(
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 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 install

1. To create a `TF-IDF` structure file for every doc, use:

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
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:

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 :

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 :

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:

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`, this module will return **0** if everythinig worked as expected. uses sample data provided [here]( 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


by [@Anurag](

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