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twitter.py
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twitter.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Apr 17 20:10:38 2020
@author: ABHIJIT SHOW
"""
import tweepy
import re
from textblob import TextBlob
import pandas as pd
from wordcloud import WordCloud, STOPWORDS
import time
from configparser import ConfigParser
import os
def tweet():
#clear the contents of wordcloud folder
clear_folder()
#Read config.ini file
config_object = ConfigParser()
config_object.read("config.ini")
twitter_cred = config_object["TWITTER"]
access_token = twitter_cred["access_token"]
access_token_secret = twitter_cred["access_token_secret"]
consumer_key = twitter_cred["consumer_key"]
consumer_secret = twitter_cred["consumer_secret"]
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
tweets = api.search(q=["coronavirus","covid19"], lang="en", count=100, tweet_mode = 'extended',
result_type = "recent")
tweets_list = []
sentences = []
count = 0
for tweet in tweets:
count = count + 1
clean_tweet = tweet.full_text
clean_tweet = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|'\
'(?:%[0-9a-fA-F][0-9a-fA-F]))+','', clean_tweet)
clean_tweet = re.sub("(@[A-Za-z0-9_]+)","", clean_tweet)
sentences.append(clean_tweet[5:])
if TextBlob(clean_tweet).sentiment.polarity > 0:
testimonial = "Positive"
elif TextBlob(clean_tweet).sentiment.polarity == 0:
testimonial = "Neutral"
elif TextBlob(clean_tweet).sentiment.polarity < 0:
testimonial = "Negative"
tweets_list.append({"count":count,"name":tweet.user.name,"tweet":clean_tweet,
"tweet_sentiment":testimonial})
papers = pd.DataFrame(sentences)
papers.columns=['paper_text_processed']
# Convert the titles to lowercase
papers['paper_text_processed'] = papers['paper_text_processed'].map(lambda x: x.lower())
# Join the different processed titles together.
long_string = ','.join(list(papers['paper_text_processed'].values))
#Create set of Stopwords
stopwords = set(STOPWORDS)
# Create a WordCloud object
word_cloud = WordCloud(stopwords=stopwords, background_color="white", max_words=5000, contour_width=3,
contour_color='steelblue',width = 1000,height = 600)
# Generate a word cloud
word_cloud.generate(long_string)
# Visualize the word cloud
location = "static/img/wordcloud/wordcloud" + str(int(time.time()*1000000)) + ".png"
word_cloud.to_file(location)
return [{"image_location":location}] + tweets_list
def india_tweet():
#clear the contents of wordcloud folder
clear_folder()
#Read config.ini file
config_object = ConfigParser()
config_object.read("config.ini")
twitter_cred = config_object["TWITTER"]
access_token = twitter_cred["access_token"]
access_token_secret = twitter_cred["access_token_secret"]
consumer_key = twitter_cred["consumer_key"]
consumer_secret = twitter_cred["consumer_secret"]
# Authenticate to Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
# Create API object
api = tweepy.API(auth)
tweets = api.search(q=["coronavirus","covid19"], lang="en", count=100, tweet_mode = 'extended',
result_type = "recent",geocode="21.7679,78.8718,1400km")
tweets_list = []
sentences = []
count = 0
for tweet in tweets:
count = count + 1
clean_tweet = tweet.full_text
clean_tweet = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|'\
'(?:%[0-9a-fA-F][0-9a-fA-F]))+','', clean_tweet)
clean_tweet = re.sub("(@[A-Za-z0-9_]+)","", clean_tweet)
sentences.append(clean_tweet[5:])
if TextBlob(clean_tweet).sentiment.polarity > 0:
testimonial = "Positive"
elif TextBlob(clean_tweet).sentiment.polarity == 0:
testimonial = "Neutral"
elif TextBlob(clean_tweet).sentiment.polarity < 0:
testimonial = "Negative"
tweets_list.append({"count":count,"name":tweet.user.name,"tweet":clean_tweet,
"tweet_sentiment":testimonial})
papers = pd.DataFrame(sentences)
papers.columns=['paper_text_processed']
# Convert the titles to lowercase
papers['paper_text_processed'] = papers['paper_text_processed'].map(lambda x: x.lower())
# Join the different processed titles together.
long_string = ','.join(list(papers['paper_text_processed'].values))
#Create set of Stopwords
stopwords = set(STOPWORDS)
# Create a WordCloud object
word_cloud = WordCloud(stopwords=stopwords, background_color="white", max_words=5000, contour_width=3,
contour_color='steelblue',width = 1000,height = 600)
# Generate a word cloud
word_cloud.generate(long_string)
# Visualize the word cloud
location = "static/img/wordcloud/wordcloud" + str(int(time.time()*1000000)) + ".png"
word_cloud.to_file(location)
return [{"image_location":location}] + tweets_list
def clear_folder():
mydir = "static/img/wordcloud/"
filelist = [ f for f in os.listdir(mydir)]
for f in filelist:
os.remove(os.path.join(mydir, f))
# tweet()
india_tweet()