Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences

Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences Hong Yu Department of Computer Science Columbia University New York, NY 10027, USA hongyu@cs.columbia.edu Vasileios Hatzivassiloglou Department of Computer Science Columbia University New York, NY 10027, USA vh@cs.columbia.edu Abstract Opinion question answering is a challenging task for natural language processing. In this paper, we discussa necessarycomponentfor an opinion ques- tion answering system: separating opinions from fact, at both the document and sentence level. We present a Bayesian classifier for discriminating be- tween documents with a preponderanceof opinions such as editorials from regular news stories, and describe three unsupervised, statistical techniques for the significantly harder task of detecting opin- ions at the sentence level. We also present a first model for classifying opinion sentences as positive or negative in terms of the main perspective be- ing expressed in the opinion. Results from a large collection of news stories and a human evaluation of 400 sentences are reported, indicating that we achieve very high performance in document classi- fication (upwards of 97% precision and recall), and respectable performance in detecting opinions and classifying them at the sentence level as positive, negative, or neutral (up to 91% accuracy). 1 Introduction Newswire articles include those that mainly present opinions or ideas, such as editorials and letters to the editor, and those that mainly report facts such as daily news articles. Text materials from many other sources also contain mixed facts and opinions. For many natural language processing applications, the ability to detect and classify factual and opinion sen- tences offers distinctadvantages in deciding what in- formation to extract and how to organize and present this information. For example, information extrac- tion applications may target factual statements rather than subjective opinions, and summarization sys- tems may list separately factual information and ag- gregate opinions according to distinct perspectives. At the document level, information retrieval systems can target particular types of articles and even utilize perspectives in focusing queries (e.g., filtering or re- trieving only editorials in favor of a particular policy decision). Our motivation for building the opinion detec- tion and classification system described in this pa- per is the need for organizing information in the context of question answering for complex ques- tions. Unlike questions like “Who was the first man on the moon?” which can be answered with a simple phrase, more intricate questions such as “What are the reasons for the US-Iraq war?” require long answers that must be constructed from multi- ple sources. In such a context, it is imperative that the question answering system can discriminate be- tween opinions and facts, and either use the appro- priate type depending on the question or combine them in a meaningful presentation. Perspective in- formation can also help highlight contrasts and con- tradictions between different sources—there will be significant disparity in the material collected for the question mentioned above between Fox News and the Independent, for example. Fully analyzing and classifying opinions involves tasks that relate to some …

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