import tweepy

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

analyser = SentimentIntensityAnalyzer()

import preprocessor as p

import re

import matplotlib.pyplot as plt

#add your own keys

CONSUMER_KEY = ""

CONSUMER_SECRET = ""

ACCESS_TOKEN =""

ACCESS_TOKEN_SECRET=""

auth = tweepy.OAuthHandler(consumer_key = CONSUMER_KEY,consumer_secret = CONSUMER_SECRET)

auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

api = tweepy.API(auth,wait_on_rate_limit=True)

def perc_response(a):

p_a = 100 * (len(a) / 57)

return round(p_a, 2)

search_input = input("Enter your first keyword/hash you want to analyze:")

search_input1 = input("Enter your second keyword/hash you want to analyze:")

all_tweets = []

for tweet in tweepy.Cursor(api.search, q=search_input, tweet_mode='extended', lang="en", result_type='recent').items(57):

all_tweets.append(tweet.full_text)

tweets_clean = []

for tweet in all_tweets:

tweet = p.clean(tweet)

tweet = ' '.join(re.sub(':', ' ', tweet).split())

tweets_clean.append(tweet)

positive_l = []

negative_l = []

neutral_l = []

for tweet in tweets_clean:

if analyser.polarity_scores(tweet).get('compound') >= 0.05:

positive_l.append(tweet)

elif analyser.polarity_scores(tweet).get('compound') <= -0.05:

negative_l.append(tweet)

else:

neutral_l.append(tweet)

positive = perc_response(positive_l)

negative = perc_response(negative_l)

neutral = perc_response(neutral_l)

# For the second hash word

all_tweets1 = []

for tweet in tweepy.Cursor(api.search, q=search_input1, tweet_mode='extended', lang="en", result_type='recent').items(57):

all_tweets1.append(tweet.full_text)

tweets_clean1 = []

for tweet in all_tweets1:

tweet = p.clean(tweet)

tweet = ' '.join(re.sub(':', ' ', tweet).split())

tweets_clean1.append(tweet)

positive_l1 = []

negative_l1 = []

neutral_l1 = []

for tweet in tweets_clean1:

if analyser.polarity_scores(tweet).get('compound') >= 0.05:

positive_l1.append(tweet)

elif analyser.polarity_scores(tweet).get('compound') <= -0.05:

negative_l1.append(tweet)

else:

neutral_l1.append(tweet)

positive1 = perc_response(positive_l1)

negative1 = perc_response(negative_l1)

neutral1 = perc_response(neutral_l1)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 10)) # ax1,ax2 refer to your two pies

labels = ['Positive', 'negative', 'neutral']

values = [positive, negative, neutral]

colors = ['green', 'red', 'yellow']

explode = (0, 0.1, 0.1)

ax1.pie(values, labels=labels, colors=colors, explode=explode, autopct='%1.1f%%', shadow=True, startangle=90) # plot first pie

ax1.set_title('Peoples sentiment on the topic '+search_input+' is:')

labels1 = ['Positive', 'negative', 'neutral']

values1 = [positive1, negative1, neutral1]

colors = ['green', 'red', 'yellow']

explode = (0, 0.1, 0.1)

ax2.pie(values1, labels=labels1, colors=colors,explode=explode, autopct='%1.1f%%', shadow=True,startangle=90) # plot second pie

plt.title('Peoples sentiment on the topic ' + search_input1 + ' is:')

if (positive > positive1):

print("Based on the sentiment's analyzed for the above keyword/hash submitted: ")

print(f'{search_input} is better than {search_input1}.')

else:

print("Based on the sentiment's analyzed for the above keyword/hash submitted: ")

print(f'{search_input1} is better than {search_input}.')

#plotting

plt.show()