Twitter Sentiment Analysis WebApp using Flask (original) (raw)
`from flask import Blueprint, render_template, request import matplotlib.pyplot as plt import os import tweepy import csv import re from textblob import TextBlob import matplotlib matplotlib.use('agg')
Register this file as a blueprint
second = Blueprint("second", name, static_folder="static", template_folder="template")
Render page when URL is called
@second.route("/sentiment_analyzer") def sentiment_analyzer(): return render_template("sentiment_analyzer.html")
Class with main logic
class SentimentAnalysis:
def __init__(self):
self.tweets = []
self.tweetText = []
# This function connects to the Tweepy API and downloads tweet data
def DownloadData(self, keyword, tweets):
# Authenticating
consumerKey = 'get your consumerkey from X (twitter) developer portal'
consumerSecret = 'get your consumerSecret from X (twitter) developer portal'
accessToken = 'get your accessToken from X (twitter) developer portal'
accessTokenSecret = 'get your accessTokenSecret from X (twitter) developer portal'
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
api = tweepy.API(auth, wait_on_rate_limit=True)
# Convert tweets to integer
tweets = int(tweets)
# Searching for tweets (Updated to use api.search_tweets)
self.tweets = tweepy.Cursor(
api.search_tweets, q=keyword, lang="en").items(tweets)
# Open/create a file to append data to
csvFile = open('result.csv', 'a')
# Use csv writer
csvWriter = csv.writer(csvFile)
# Creating variables to store sentiment info
polarity = 0
positive = 0
wpositive = 0
spositive = 0
negative = 0
wnegative = 0
snegative = 0
neutral = 0
# Iterating through tweets fetched
for tweet in self.tweets:
# Append cleaned tweet text to list
self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8'))
analysis = TextBlob(tweet.text)
polarity += analysis.sentiment.polarity
# Categorize sentiment
if analysis.sentiment.polarity == 0:
neutral += 1
elif 0 < analysis.sentiment.polarity <= 0.3:
wpositive += 1
elif 0.3 < analysis.sentiment.polarity <= 0.6:
positive += 1
elif 0.6 < analysis.sentiment.polarity <= 1:
spositive += 1
elif -0.3 < analysis.sentiment.polarity <= 0:
wnegative += 1
elif -0.6 < analysis.sentiment.polarity <= -0.3:
negative += 1
elif -1 < analysis.sentiment.polarity <= -0.6:
snegative += 1
# Write to csv and close file
csvWriter.writerow(self.tweetText)
csvFile.close()
# Calculate percentages
positive = self.percentage(positive, tweets)
wpositive = self.percentage(wpositive, tweets)
spositive = self.percentage(spositive, tweets)
negative = self.percentage(negative, tweets)
wnegative = self.percentage(wnegative, tweets)
snegative = self.percentage(snegative, tweets)
neutral = self.percentage(neutral, tweets)
# Calculate average polarity
polarity = polarity / tweets
# Determine overall sentiment for HTML display
if polarity == 0:
htmlpolarity = "Neutral"
elif 0 < polarity <= 0.3:
htmlpolarity = "Weakly Positive"
elif 0.3 < polarity <= 0.6:
htmlpolarity = "Positive"
elif 0.6 < polarity <= 1:
htmlpolarity = "Strongly Positive"
elif -0.3 < polarity <= 0:
htmlpolarity = "Weakly Negative"
elif -0.6 < polarity <= -0.3:
htmlpolarity = "Negative"
elif -1 < polarity <= -0.6:
htmlpolarity = "Strongly Negative"
# Generate pie chart
self.plotPieChart(positive, wpositive, spositive, negative,
wnegative, snegative, neutral, keyword, tweets)
print(polarity, htmlpolarity)
return polarity, htmlpolarity, positive, wpositive, spositive, negative, wnegative, snegative, neutral, keyword, tweets
def cleanTweet(self, tweet):
# Remove links, special characters, etc. from tweet
return ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)", " ", tweet).split())
# Function to calculate percentage
def percentage(self, part, whole):
temp = 100 * float(part) / float(whole)
return format(temp, '.2f')
# Function to plot and save pie chart
def plotPieChart(self, positive, wpositive, spositive, negative, wnegative, snegative, neutral, keyword, tweets):
fig = plt.figure()
labels = ['Positive [' + str(positive) + '%]', 'Weakly Positive [' + str(wpositive) + '%]',
'Strongly Positive [' + str(spositive) + '%]', 'Neutral [' + str(neutral) + '%]',
'Negative [' + str(negative) + '%]', 'Weakly Negative [' + str(wnegative) + '%]',
'Strongly Negative [' + str(snegative) + '%]']
sizes = [positive, wpositive, spositive, neutral, negative, wnegative, snegative]
colors = ['yellowgreen', 'lightgreen', 'darkgreen', 'gold', 'red', 'lightsalmon', 'darkred']
patches, texts = plt.pie(sizes, colors=colors, startangle=90)
plt.legend(patches, labels, loc="best")
plt.axis('equal')
plt.tight_layout()
strFile = r"C:\Users\LENOVO\PycharmProjects\SentimentAnalysis\static\images\plot1.png"
if os.path.isfile(strFile):
os.remove(strFile)
plt.savefig(strFile)
plt.close() # Close figure to free memory@second.route('/sentiment_logic', methods=['POST', 'GET']) def sentiment_logic(): # Get user input from HTML form keyword = request.form.get('keyword') tweets = request.form.get('tweets') sa = SentimentAnalysis()
# Set variables for Jinja template
polarity, htmlpolarity, positive, wpositive, spositive, negative, wnegative, snegative, neutral, keyword1, tweet1 = sa.DownloadData(
keyword, tweets)
return render_template('sentiment_analyzer.html', polarity=polarity, htmlpolarity=htmlpolarity, positive=positive,
wpositive=wpositive, spositive=spositive, negative=negative, wnegative=wnegative,
snegative=snegative, neutral=neutral, keyword=keyword1, tweets=tweet1)@second.route('/visualize') def visualize(): return render_template('PieChart.html')
`