Demo: Sentiment Analysis on Twitter

Now let’s try using sentiment analysis (and loop variables) on Twitter:

We’ll start by doing our normal steps to load tweepy (or fake tweepy)

Tweepy Setup

import tweepy

(optional) make a fake twitter connection with the fake_tweepy library

For testing purposes, we’ve added this line of code, which loads a fake version of tweepy, so it wont actually connect to twitter. If you want to try to actually connect to twitter, don’t run this line of code.

%run ../../fake_tweepy/fake_tweepy.ipynb

Fake tweepy is replacing the tweepy library. Fake Tweepy doesn't need real passwords, and prevents you from accessing real twitter

# Load all your developer access passwords into Python
# TODO: Put your twitter account's special developer access passwords below:
bearer_token = "n4tossfgsafs_fake_bearer_token_isa53#$%$"
consumer_key = "sa@#4@fdfdsa_fake_consumer_key_$%DSG#%DG"
consumer_secret = "45adf$T$A_fake_consumer_secret_JESdsg"
access_token = "56sd5Ss4tsea_fake_access_token_%YE%hDsdr"
access_token_secret = "j^$dr_fake_consumer_key_^A5s#DR5s"

# Give the tweepy code your developer access passwords so
# it can perform twitter actions
client = tweepy.Client(
   bearer_token=bearer_token,
   consumer_key=consumer_key, consumer_secret=consumer_secret,
   access_token=access_token, access_token_secret=access_token_secret
)

Fake Tweepy is pretending to log in to twitter

Sentiment Analysis

load sentiment analysis library and make analyzer

import nltk
nltk.download(["vader_lexicon"])
from nltk.sentiment import SentimentIntensityAnalyzer
sia = SentimentIntensityAnalyzer()

[nltk_data] Downloading package vader_lexicon to
[nltk_data]     C:\Users\kmthayer\AppData\Roaming\nltk_data...
[nltk_data]   Package vader_lexicon is already up-to-date!

loop through tweets, finding average sentiment

We can now combine our previous examples of looping through tweets with what we just learned of sentiment analysis and looping variables to find the average sentiment of a set of tweets.

query = '"cute cat"'
tweets = client.search_recent_tweets(query=query, max_results=10)

num_tweets = 0
total_sentiment = 0

# go through each tweet
for tweet in tweets.data:
    
    #calculate sentiment
    tweet_sentiment = sia.polarity_scores(tweet.text)["compound"]
    num_tweets += 1
    total_sentiment += tweet_sentiment

    print("Sentiment: " + str(tweet_sentiment))
    print("   Tweet: " + tweet.text)
    print()


average_sentiment = total_sentiment / num_tweets
print("Average sentiment was " + str(average_sentiment))

Fake Tweepy is pretending to search for '"cute cat"' and is returning some fake tweets.

Sentiment: -0.2865
   Tweet: While trying to tweet right now, I am being attacked by my cute cat! It's so hard to tpye wihsaoae as fesadf asd fssasaf sa

Sentiment: 0.8172
   Tweet: I wish I could be sleeping now like my cute cat is!

Sentiment: -0.6088
   Tweet: Why won't my cute cat stop scratching my face in the morning!

Average sentiment was -0.026033333333333315

We can now see the average sentiment of a set of tweets based on our search of twitter!

If you use your twitter bot keys, you can change the query to be whatever twitter search you want and see whether people are tweeting positively or negatively about it.

note: You can change max_results=10 to go up to 100 to get more tweets at a time to find the average of