Sentiment Analysis is a special case of text classification where users’ opinions or sentiments regarding a product are classified into predefined categories such as positive, negative, neutral etc. Public sentiments can then be used for corporate decision making regarding a product which is being liked or disliked by the public.
Both rule-based and statistical techniques have been developed for sentimental analysis. With the advancements in Machine Learning and natural language processing techniques, Sentiment Analysis techniques have improved a lot.
In this tutorial, you will see how Sentiment Analysis can be performed on live Twitter data. The tutorial is divided into two major sections: Scraping Tweets from Twitter and Performing Sentiment Analysis.
I am Machine Learning and Data Science expert currently pursuing my PhD in Computer Science from Normandy University, France.