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 API: Extracting Tweets with Specific Phrase

Twitter has been a good source for Data Mining. Many data scientists and analytics companies collect tweets and analyse them to understand people’s opinion about some matters.

In this tutorial, you will learn how to use Twitter API and Python Tweepy library to search for a word or phrase and extract tweets that include it and print the results.

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