The aim of this project is to use Python's nltk library to perform natural language processing in order to detect the emotions in the user's tweets and generate a sentiment score. This project uses tweepy for handling Twitter API calls get a particular user's tweets.
Uses Twitter's API through tweepy to get tweets of a particular twitter handle. The user can input the the desired twitter handle and a 'tweets.csv' file is generated containing the tweets of the entered twitter handle.
Uses Python's nltk library to output a sentiment score of the tweets. Preprocessing is done using the nltk.corpus stopwords and nltk.word_tokenize followed by emotion detection for the tweets. The 'emotions_count.png' is a graph showing the prominent emotions present in the user's tweets. Finally the sentiment score is calculated with nltk's SentimentIntensityAnalyzer module.
For this example I have used Lebron James' latest tweets (as of 20 August 2020) https://twitter.com/KingJames?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor
