pa_sentiment_analysis.txt

# -*- coding: utf-8 -*-
"""PA: Sentiment Analysis.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1Tylhg6mMPb5xQX8xAlKQt6fsOaUNvpbx
"""

#Description: A Sentiment Analysis Program

import tweepy
from textblob import TextBlob
#from wordcloud import WordCloud
#import pandas as pd
#import numpy as np
#import matplotlib.pyplot as plt
#plt.style.use('fivethirtyeight')

#loading data
from google.colab import files
uploaded = files.upload()

#get data
mykeys = open('keys.txt','r').read().splitlines()

# Twitter API Credentials
api_key = mykeys[0].split()[-1]
api_key_secret = mykeys[1].split()[-1]
access_token = mykeys[2].split()[-1]
access_token_secret = mykeys[3].split()[-1]

# Connecting to the API
auth_handler = tweepy.OAuthHandler(consumer_key = api_key, consumer_secret = api_key_secret)
#set access to the access token
auth_handler.set_access_token(access_token, access_token_secret)
# creating the API
api = tweepy.API(auth_handler)

#search_term = 'stocks'
search_term = input('\nEnter the word to search: ')
tweet_amount = 200
polarity = 0 #indicates if the text is positive or negative
positive, negative, neutral = 0, 0, 0

tweets = tweepy.Cursor(api.search, q = search_term, lang = 'en').items(tweet_amount)
for tweet in tweets:
  final_text = tweet.text.replace('RT', '')
  if final_text.startswith(' @'):
    position = final_text.index(':')
    final_text = final_text[position+2:]
  if final_text.startswith('@'):
    position = final_text.index(' ')
    final_text = final_text[position+2:]
  #print(final_text)
  analysis = TextBlob(final_text)
  polarity += analysis.polarity
  tweet_polarity = analysis.polarity
  if tweet_polarity > 0:
    positive += 1
  elif tweet_polarity < 0: 
    negative += 1
  else:
    neutral += 1
    polarity += tweet_polarity

print('overall polarity ',polarity)
print('positive pol ', positive)
print('negative pol ', negative)
print('neutral pol ', neutral)