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.
For example, REF collected web users' messages posted on an election prediction website (www.electionprediction.org) to automatically build a gold standard corpus.
Crystal: Analyzing Predictive Opinions on the Web
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science