Publication Type

Working Paper

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Sentiment analysis is a new area in text analytics where it focuses on the analysis and understanding of the human emotions from the text patterns. This new form of analysis has been widely adopted in customer relationship management especially in the context of complaint management. However, sentiment analysis using Twitter data has remained extremely difficult to manage due to sampling biasness. In this paper, we will discuss about the application of reweighting techniques in conjunction with online sentiment divisions to predict the vote percentage that individual presidential candidate in Singapore will receive in the Presidential Election 2011. There will be in depth discussion about the various aspects using sentiment analysis to predict outcomes as well as the potential pitfalls in the estimation due to the anonymous nature of the Internet. Our methodology was successful in predicting the top two contenders in a four-corner fight, and that there would be a thin margin between them. Our modified result was able to predict the winner with swing voters’ estimation using cluster analysis. However, the final predicted values still differ from actual values due to astroturfing, which is extremely difficult to estimate and will be recommended for future work.


Twitter, Sentiment Analysis, Presidential Election, Singapore, Census


Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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