Predicting the winner of Delhi assembly election, 2015 from sentiment analysis on twitter data : a bigdata perspective

Abstract EN

: Social media is currently a place where people create and share contents at a massive rate.

Because of its ease of use, speed and reach, it is fast changing the public discourse in society and setting trends and agendas in different topics including environment, politics technology, entertainment etc.

As it is a form of collective wisdom, we decided to investigate its power at predicting real-world outcomes.

The objective was to design a Twitter-based sentiment mining.

We introduce a keyword-aware user-based collective tweet mining approach to rank the sentiment of each user.

To prove the accuracy of this method, we chose an Election Winner Prediction application and observed how the sentiments of people on different political issues at that time got reflected in their votes.

A Domain thesaurus is built by collecting keywords related to each issue.

Twitter data being huge in size and difficult to process, we use a scalable and efficient Map Reduce programming model-based approach, to classify the tweets.

The experiments were designed to predict the winner of Delhi Assembly Elections 2015, by analyzing the sentiments of people on political issues and from this analysis, we accurately predicted that Aam Admi Party has a higher support, compared to Bharathiya Janatha Party (BJP), the ruling party.

Thus, a Big Data Approach that has widespread applications in today’s world, is used for sentiment analysis on Twitter data