##Overview
This is the code for the Twitter Sentiment Analyzer challenge for 'Learn Python for Data Science #2' by @Sirajology on YouTube. The code uses the tweepy library to access the Twitter API and the TextBlob library to perform Sentiment Analysis on each Tweet. We'll be able to see how positive or negative each tweet is about whatever topic we choose.
##Dependencies
- tweepy (http://www.tweepy.org/)
- textblob (https://textblob.readthedocs.io/en/dev/)
Install missing dependencies using pip
##Usage
Once you have your dependencies installed via pip, run the script in terminal via
python demo.py
##Challenge
Last week, there was the french Republicans Primary debate on television. I tried to apply Siraj's sentiment analysis to this night of debate in order to compare the seven different candidates. Here is the result of the analysis :
Mean Sentiment Polarity in descending order :
- Poisson : 0.180
- Fillon : 0.113
- Juppe : 0.098
- Sarkozy : 0.057
- Cope : 0.036
- Le Maire : 0.007
- Kosciusko : 0.007
##Credits
This code is forked from Siraj