Why Question Answering using Sentiment Analysis and Word Classes

In this paper we explore the utility of sentiment analysis and semantic word classes for improving why-question answering on a large-scale web corpus. Our work is motivated by the observation that a why-question and its answer often follow the pattern that if something undesirable happens, the reason is also often something undesirable, and if something desirable happens, the reason is also often something desirable. To the best of our knowledge, this is the first work that introduces sentiment analysis to non-factoid question answering. We combine this simple idea with semantic word classes for ranking answers to why-questions and show that on a set of 850 why-questions our method gains 15.2% improvement in precision at the top-1 answer over a baseline state-of-the-art QA system that achieved the best performance in a shared task of Japanese non-factoid QA in NTCIR-6.