Crystal: Analyzing Predictive Opinions on the Web

An election prediction system based on web users’ opinions posted on an election prediction website, which significantly outperforms several baselines as well as a non-generalized n-gram approach and proposes a novel technique which generalizes n- gram feature patterns.

Abstract

In this paper, we present an election prediction system (Crystal) based on web users’ opinions posted on an election prediction website. Given a prediction message, Crystal first identifies which party the message predicts to win and then aggregates prediction analysis results of a large amount of opinions to project the election results. We collect past election prediction messages from the Web and automatically build a gold standard. We focus on capturing lexical patterns that people frequently use when they express their predictive opinions about a coming election. To predict election results, we apply SVM-based supervised learning. To improve performance, we propose a novel technique which generalizes n-gram feature patterns. Experimental results show that Crystal significantly outperforms several baselines as well as a non-generalized n-gram approach. Crystal predicts future elections with 81.68 % accuracy. 1