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HANDS-ON TUTORIALS, MACHINE LEARNING PROJECT
How to Build a Twitter Sentiment Analysis System
Applying natural language processing for sentiment analysis
In the field of social media data analytics, one popular area of research is the sentiment analysis of Twitter data. Twitter is one of the most popular social media platforms in the world, with 330 million monthly active users and 500 million tweets sent each day. By carefully analyzing the sentiment of these tweets — whether they are positive, negative, or neutral, for example — we can learn a lot about how people feel about certain topics.
Understanding the sentiment of tweets is important for a variety of reasons: business marketing, politics, public behavior analysis, and information gathering are just a few examples. Sentiment analysis of Twitter data can help marketers understand the customer response to product launches and marketing campaigns, and it can also help political parties understand the public response to policy changes or announcements.
However, Twitter data analysis is no simple task. There are something like ~6000 tweets released every second. That’s a lot of Twitter data! And though it’s easy for humans to interpret the sentiment of a tweet, human sentiment analysis is simply not scalable.
