A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction

TL;DR

This paper explores how Twitter sentiment analysis, combined with census correction techniques, can be used to predict election outcomes, addressing sampling bias and data anonymity challenges.

Contribution

It introduces a reweighting approach with online sentiment analysis to improve election prediction accuracy from Twitter data.

Findings

Reweighting techniques improve prediction accuracy

Census correction mitigates sampling bias

Method addresses anonymity issues in social media data

Abstract

Sentiment analysis is a new area in text analytics where it focuses on the analysis and understanding of the emotions from the text patterns. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. With increasing level of interest in this technology, more and more companies are adopting it and using it to champion their marketing efforts. However, sentiment analysis using twitter has remained extremely difficult to manage due to the sampling bias. In this paper, we will discuss about the application of using reweighting techniques in conjunction with online sentiment divisions to predict the vote percentage that individual candidate will receive. 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.