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OPTIMIZED CODE AND CREDENTIALS PROTECTED #5

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124 changes: 56 additions & 68 deletions main.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,12 @@
import sys,tweepy,csv,re
import tweepy
import csv
import re
from collections import Counter
from textblob import TextBlob
import matplotlib.pyplot as plt
import os
from dotenv import load_dotenv
load_dotenv()


class SentimentAnalysis:
Expand All @@ -10,91 +16,73 @@ def __init__(self):
self.tweetText = []

def DownloadData(self):

# Importing keys from .env file
CONSUMER_KEY = os.getenv('CONSUMER_KEY')
CONSUMER_SECRET = os.getenv('CONSUMER_SECRET')
ACCESS_TOKEN = os.getenv('ACCESS_TOKEN')
ACCESS_TOKEN_SECRET = os.getenv('ACCESS_TOKEN_SECRET')


# authenticating
consumerKey = 'your key here'
consumerSecret = 'your key here'
accessToken = 'your key here'
accessTokenSecret = 'your key here'
auth = tweepy.OAuthHandler(consumerKey, consumerSecret)
auth.set_access_token(accessToken, accessTokenSecret)
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)
api = tweepy.API(auth)

# input for term to be searched and how many tweets to search
searchTerm = input("Enter Keyword/Tag to search about: ")
NoOfTerms = int(input("Enter how many tweets to search: "))

# searching for tweets
self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang = "en").items(NoOfTerms)
self.tweets = tweepy.Cursor(api.search, q=searchTerm, lang="en").items(NoOfTerms)

# Open/create a file to append data to
csvFile = open('result.csv', 'a')

# Use csv writer
csvWriter = csv.writer(csvFile)


# creating some variables to store 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 to temp so that we can store in csv later. I use encode UTF-8
self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8'))
# print (tweet.text.translate(non_bmp_map)) #print tweet's text
analysis = TextBlob(tweet.text)
# print(analysis.sentiment) # print tweet's polarity
polarity += analysis.sentiment.polarity # adding up polarities to find the average later

if (analysis.sentiment.polarity == 0): # adding reaction of how people are reacting to find average later
neutral += 1
elif (analysis.sentiment.polarity > 0 and analysis.sentiment.polarity <= 0.3):
wpositive += 1
elif (analysis.sentiment.polarity > 0.3 and analysis.sentiment.polarity <= 0.6):
positive += 1
elif (analysis.sentiment.polarity > 0.6 and analysis.sentiment.polarity <= 1):
spositive += 1
elif (analysis.sentiment.polarity > -0.3 and analysis.sentiment.polarity <= 0):
wnegative += 1
elif (analysis.sentiment.polarity > -0.6 and analysis.sentiment.polarity <= -0.3):
negative += 1
elif (analysis.sentiment.polarity > -1 and analysis.sentiment.polarity <= -0.6):
snegative += 1


# Write to csv and close csv file
csvWriter.writerow(self.tweetText)
csvFile.close()

# finding average of how people are reacting
positive = self.percentage(positive, NoOfTerms)
wpositive = self.percentage(wpositive, NoOfTerms)
spositive = self.percentage(spositive, NoOfTerms)
negative = self.percentage(negative, NoOfTerms)
wnegative = self.percentage(wnegative, NoOfTerms)
snegative = self.percentage(snegative, NoOfTerms)
neutral = self.percentage(neutral, NoOfTerms)

with open('result.csv', 'a') as csvFile:
# Use csv writer
csvWriter = csv.writer(csvFile)

# iterating through tweets fetched
for tweet in self.tweets:
#Append to temp so that we can store in csv later. I use encode UTF-8
self.tweetText.append(self.cleanTweet(tweet.text).encode('utf-8'))
# print (tweet.text.translate(non_bmp_map)) #print tweet's text
analysis = TextBlob(tweet.text)
# print(analysis.sentiment) # print tweet's polarity
self.sentiments.append(analysis.sentiment.polarity)

# Write to csv
csvWriter.writerow(self.tweetText)

# calculate sentiment percentages
sentiment_counts = Counter(
'strongly positive' if s > 0.6 and s <= 1 else
'strongly negative' if s >= -1 and s <= -0.6 else
'positive' if s > 0.3 and s <= 0.6 else
'weakly positive' if 0.3 >= s > 0 else
'neutral' if s==0 else
'weakly negative' if 1 >= s > -0.3 else
'negative' if 0.6 >= s > -1 else 'other'
for s in self.sentiments)
positive = self.percentage(sentiment_counts['positive'], NoOfTerms)
wpositive = self.percentage(sentiment_counts['weakly positive'], NoOfTerms)
negative = self.percentage(sentiment_counts['negative'], NoOfTerms)
wnegative = self.percentage(sentiment_counts['weakly negative'], NoOfTerms)
spositive = self.percentage(sentiment_counts['strongly positive'], NoOfTerms)
snegative = self.percentage(sentiment_counts['strongly negative'], NoOfTerms)
neutral = self.percentage(sentiment_counts['neutral'], NoOfTerms)
# finding average reaction
polarity = polarity / NoOfTerms
polarity = sum(self.sentiments) / NoOfTerms

# printing out data
print("How people are reacting on " + searchTerm + " by analyzing " + str(NoOfTerms) + " tweets.")
print(f"How people are reacting on {searchTerm} by analyzing {NoOfTerms} tweets.")
print()
print("General Report: ")

if (polarity == 0):
if polarity == 0:
print("Neutral")
elif (polarity > 0 and polarity <= 0.3):
elif polarity > 0 and polarity <= 0.3:
print("Weakly Positive")
elif (polarity > 0.3 and polarity <= 0.6):
elif polarity > 0.3 and polarity <= 0.6:
print("Positive")
elif (polarity > 0.6 and polarity <= 1):
print("Strongly Positive")
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