Twitter sentiment analysis using Python and NLTK
This post describes the implementation of sentiment analysis of tweets using Python and the natural language toolkit NLTK. The post also describes the internals of NLTK related to this implementation. Background The purpose of the implementation is to be able to automatically classify a tweet as a positive or negative tweet sentiment wise. The classifier needs to be trained and to do that, we need a list of manually classified tweets. Positive tweets: I love this car.This view is amazing.I feel great this morning.I am so excited about the concert.He is my best friend. Negative tweets: I do not like this car.This view is horrible.I feel tired this morning.I am not looking forward to the concert.He is my enemy. In the full implementation, I use about 600 positive tweets and 600 negative tweets to train the classifier. Next is a test set so we can assess the exactitude of the trained classifier. Test tweets: Implementation The following list contains the positive tweets: Classifier Classify Voilà.