This file extract the event names from given pieace of text/tweet/article.
I. First we do tokenization - converting sentences into words called tokenization
   nltk.word_tokenize()

II. Before doing tokenization we have to do Cleaning Proces. we use regular expression to do this task

III. After Tokenization we tag individual word with part of speech.
    nltk.pos_tag()

IV. Next Step is Chunking. To do this step we first define grammer and than appply parser 
    nltk.RegexpParser(grammer)

V. Final  step is choosing event or NP phrase.

This file extract tweets ofpast 3 days.
I. In this file we work on tweepy api that is provided by twitter.
II. save relevant tweets into a /json file

This file is used to tag title of an tweet.
I. First we get tweet using tweepy api.
II. Tokenization  - Broke a sentence into words  
    nltk.word_toknize()
III. CLeaning  - remove annoying data like stopwords,emojis, hyperinks,emotions , punchuationetc.
IV. POS tagging  -  trace out which is noun or other part of speech
    nltk.pos_tag()
V. Chunking   -  Extract phrases from pos tagger. first decide grammer than apply parser.
    nltk.RegexParser(grammer)
    
VI. Chinking  - the process of removing a sequence of tokens from a chunk
    Lemmitization - nltk.wordnet.WordNetLemmatizer()
    Stemmerization - nltk.stem.PorterStemmer()
VII. Spell Checking and CLeaning
VIII. Final  step is tagging title.

Fisrt set article variable with string/pieace of text and than run a python file

  Just Run it and pass  word/phrases

call  fetch(query, no_of_tweets) function with two parameters