Predicting the Winner of Delhi Assembly Election,
2015 from Sentiment Analysis on Twitter
Data-A BigData Perspective
Lija Mohan and Sudheep
Elayidom
Division of Computer Science, Cochin University of Science and
Technology, India
Abstract: 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.
Keywords: Election winner prediction, big data,
sentiment analysis, tweet mining, map reduce.