Sentiment Analysis of Twitter Feeds

analyseTweets.py

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+import csv 
+import random
+import re
+import codecs #provides transparent encoding/decoding
+from textblob import TextBlob  #Library for Text Processing
+import time
+from collections import Counter
+
+#Plotting dependecies
+import matplotlib.pyplot as plt; plt.rcdefaults()
+import numpy as np
+import matplotlib.pyplot as plt
+
+#Sentiment values 
+positive=0
+negative=0
+neutral=0
+total=0
+
+hashtags = []
+
+#Loading.....		
+print("Performing Sentiment Analysis",end="")
+for i in range(5):
+        print(".",end="")
+        time.sleep(1) 
+
+
+# reading csv file
+
+filepath="demonetization-tweets.csv"
+with codecs.open(filepath, "r",encoding='utf-8', errors='ignore') as csvfile:
+	reader = csv.reader(csvfile)
+	tweetsList=[]
+	cleanTweetsList=[]
+	for row in reader:
+		tweet=row[2].strip() #contains tweet
+		cleanTweet=" ".join(re.findall("[a-zA-Z]+",tweet))
+		analysis=TextBlob(cleanTweet) 
+
+		#appending tweet to list 
+		tweetsList.append(tweet) 
+		cleanTweetsList.append(cleanTweet)
+
+
+		#Assigning polarity and calculating count
+		total=total+1
+		if(analysis.sentiment.polarity>0):
+			positive=positive+1
+		if(analysis.sentiment.polarity==0):
+			neutral=neutral+1
+		else:
+			negative=negative+1
+
+
+#Result
+print() #newline 
+print("Total Tweets: ",total)
+print('Positive = ',positive)
+print('Neutral= ',neutral)
+print('Negative= ',negative)
+
+#Random number generator to pick tweets
+randomTweets=[]
+randomCleanTweets=[]
+
+randomNumber=random.sample(range(1, 6000), 5)
+index=0
+
+#Collecting random tweets
+for i in range(5):
+	number=random.randint(1,6000) #Picks a random number between 1-6000
+	randomTweets.append(tweetsList[randomNumber[index]]) #Stores a random tweet from data without repeatition
+	randomCleanTweets.append(tweetsList[randomNumber[index]])
+	index=index+1
+
+for tweet in randomCleanTweets:
+	print()
+	print(tweet,end=' ')
+	analysis=TextBlob(tweet)
+	print(" => ",analysis.sentiment)
+
+
+#Writing random tweets to a text file for display
+
+with open('tweets.txt', 'w') as file:
+	for tweet in randomTweets:
+		file.write(tweet)
+		file.write('<br><br>\n')
+
+#Finding the hashtags in all the tweets
+finalcount={}
+for i in tweetsList:
+	hashtags.append(re.findall(r"#(\w+)", i))
+hashtagnew = [item for sub in hashtags for item in sub]
+counts = Counter(hashtagnew)
+counts = dict(counts)
+finalcount = dict(sorted(counts.items(), key=lambda kv: kv[1], reverse=True))
+countname = list(finalcount.keys())
+
+#Plotting data
+
+
+#Bar Graph
+objects = ('Positive','Neutral','Negative')
+y_pos = np.arange(len(objects))
+performance = [positive,neutral,negative]
+
+plt.bar(y_pos, performance, align='center', alpha=0.5)
+plt.xticks(y_pos, objects)
+plt.ylabel('# of tweets')
+plt.title('Twitter Sentiment Analysis- Demonetisation (Bar Graph) \n')
+plt.show()
+
+
+#Pie Graph
+
+colors = ['yellowgreen', 'gold', 'orangered']
+explode = (0, 0, 0.1)  # explode last slice
+
+plt.pie(performance, explode=explode, labels=objects, colors=colors,
+        autopct='%1.1f%%', shadow=False, startangle=140)
+
+plt.axis('equal')
+plt.title('Twitter Sentiment Analysis- Demonetisation (Pie Chart) \n')
+plt.show()
+
+# Hashtag Plot
+x = np.arange(len(finalcount))
+y = list(finalcount.values())
+x = x[:15]
+y = y[:15]
+countname = countname[:15]
+plt.bar(x, y)
+plt.title('Most Trending Hashtags\n')
+plt.xticks(x, countname, rotation='vertical')
+plt.ylabel('Number of tweets')
+plt.xlabel('#Hashtags')
+plt.tight_layout()
+plt.show()