Twitter Sentiment Analysis using Naive Bayes

You can follow the explanation for my code on my medium: https://medium.com/@koshut.takatsuji/twitter-sentiment-analysis-with-full-code-and-explanation-naive-bayes-a380b38f036b

This program can be followed by going through part1 through part6 sequentially.

But as a whole the general steps I take to complete this project are:

1. Part1.py : Get a twitter API and download Tweepy to access the twitter api through python
2. Part2.py : Download twitter tweet data depending on a key word search “happy” or “sad”
3. Part3.py : Format my tweets so that no capitalization, punctuation, or non ascii characters are present, as well as splitting the tweet into an array holding each word in a separate holder
4. Part4.py : Create a bag of common words that appear in my tweets
5. Part5.py : Create a frequency table of words that have positive and negative hits
6. Part6.py : Test my frequency table by using test sentences