Twitter API with Python: Part 5 - Sentiment Analysis

August 4, 2018 comments 108 Reads

In this video, we will continue with our use of the Tweepy Python module and the code that we wrote.
We will be making use of the “TextBlob” module to do some rudimentary sentiment analysis on the tweets that we stream. The sentiment analysis engine provided by TextBlob is already trained on data, so we just need to apply it to our tweet data (once we clean the tweet appropriately). We then add the sentiment analysis information into our data frame.
Relevant Links:
Part 1: https://www.youtube.com/watch?v=wlnx-7cm4Gg
Part 2: https://www.youtube.com/watch?v=rhBZqEWsZU4
Part 3: https://www.youtube.com/watch?v=WX0MDddgpA4
Part 4: https://www.youtube.com/watch?v=w9tAoscq3C4
Part 5: https://www.youtube.com/watch?v=pdnTPUFF4gA
Tweepy Website:
http://www.tweepy.org/
Cursor Docs:
http://docs.tweepy.org/en/v3.5.0/cursor_tutorial.html
API Reference:
http://docs.tweepy.org/en/v3.5.0/api.html
GitHub Code for this Video:
https://github.com/vprusso/youtube_tutorials/tree/master/twitter_python/part_5_sentiment_analysis_tweet_data
My Website:
vprusso.github.io
Do you like the development environment I’m using in this video? It’s a customized version of vim that’s enhanced for Python development. If you want to see how I set up my vim, I have a series on this here:
http://bit.ly/lp_vim
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