Skip to content

Added matplotlib visualizations #36

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
arch-raven wants to merge 3 commits into
base: master
Choose a base branch
from
Open

Added matplotlib visualizations #36

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
66b23ac
Added matplotlib visualizations
arch-raven Oct 10, 2020
eb5764f
Removed unused variable
arch-raven Oct 10, 2020
620723d
removed trailing whitespace
arch-raven Oct 10, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 12 additions & 9 deletions jobtweets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,8 @@
import tweepy
from tweepy import OAuthHandler
from textblob import TextBlob

import matplotlib.pyplot as plt

class TwitterClient(object):
'''
Generic Twitter Class for sentiment analysis.
Expand Down Expand Up @@ -88,23 +89,25 @@ def main():
api = TwitterClient()
tweets = api.get_tweets(query = 'Job Opportunities', count = 500)
ptweets = [tweet for tweet in tweets if tweet['sentiment'] == 'positive']

print("Positive tweets percentage: {} %".format(100*len(ptweets)/len(tweets)))

ntweets = [tweet for tweet in tweets if tweet['sentiment'] == 'negative']

print("Negative tweets percentage: {} %".format(100*len(ntweets)/len(tweets)))

print("Neutral tweets percentage: {} % ".format(100*(len(tweets) - len(ntweets) - len(ptweets))/len(tweets)))

plt.figure(figsize=(7, 5))
plt.pie([len(ptweets)/len(tweets), len(ntweets)/len(tweets), (len(tweets)-len(ptweets)-len(ntweets))/len(tweets)],
labels=["postive", "negative", "neutral"],
autopct='%1.1f%%',
startangle=90)
plt.title("Job Opportunities Tweets Sentiment")
plt.axis("equal")
plt.show()

print("\n\nPositive tweets:")
for tweet in ptweets[:10]:
print(tweet['text'])

print("\n\nNegative tweets:")
for tweet in ntweets[:10]:
print(tweet['text'])

if __name__ == "__main__":

main()