analyseTweets.py

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()