text: import nltk,re,time,sys	,codecs,os,requests
  import xml.etree.ElementTree as ET
  from lxml import etree
  from nltk.corpus import stopwords
  from textblob import TextBlob
  from selenium import webdriver
  from selenium.webdriver import ActionChains
  from selenium.webdriver.common.by import By
  from selenium.webdriver.common.keys import Keys
  from xml.dom import minidom
  from tqdm import tqdm
  import pandas as pd
  from pandas import ExcelWriter
  from bs4 import BeautifulSoup
  from selenium.webdriver.support.ui import WebDriverWait
  from selenium.webdriver.support import expected_conditions as ec
  from contextlib import closing
  from selenium.common.exceptions import TimeoutException
  from selenium.webdriver.common.action_chains import ActionChains
  from selenium.webdriver import Firefox
  from src.majority import find_majority_terms
  from src.percentage import percentage
  from src.remove_non_ascii import remove_non_ascii_1
  def word_maini(stri,f_name):
  	total_count = 0
  	positive_tweets = []
  	negative_tweets = []
  	neutral_tweets = []
  	major_terms = []
  	positive =0
  	negative =0
  	neutral = 0
  	major_terms.append(stri)
  	doc = etree.XMLParser(recover=True)
  	tree = etree.parse(f_name,doc)
  	for df in tree.xpath('//review'):
  			subfields = df.getchildren()
  			sentences = nltk.sent_tokenize(str(subfields[0].text))
  			for term in major_terms:
  				for sentence in sentences:
  					words = nltk.word_tokenize(sentence)
  					# print("--WORDS--\n")
  					# print(words)
  					if term in words:
  						analysis = TextBlob(sentence)
  						if(analysis.sentiment.polarity == 0):
  							neutral +=1
  							neutral_tweets.append(sentence)
  						elif(analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <=1):
  							positive +=1
  							positive_tweets.append(sentence)
  						elif(analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <=0):
  							negative+=1
  							negative_tweets.append(sentence)
  						total_count +=1
  	print("coount iis "+str(total_count))
  	return negative_tweets,positive_tweets,neutral_tweets