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Scraping Tweets and Performing Sentiment Analysis

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.

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Twitter Sentiment Analysis Using TF-IDF Approach

Text Classification is a process of classifying data in the form of text such as tweets, reviews, articles, and blogs, into predefined categories. Sentiment analysis is a special case of Text Classification where users’ opinion or sentiments about any product are predicted from textual data.

In this tutorial, you will learn how to develop a Sentiment Analysis model that will use TF-IDF feature generation approach and will be capable of predicting user sentiment (i.e. view or opinion that is held or expressed) about 6 Airlines operating in the United States through analysing user tweets. You will use Python’s  Scikit-Learn library  for machine learning to implement the TF-IDF approach and to train our prediction model.

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