There has been a recent swell of interest in the automatic identification and extraction of opinions, emotions, and sentiments in text. Motivation for this task comes from the desire to provide tools for information analysts in government, commercial, and political domains, who want to automatically track attitudes and feelings in the news and on-line forums. How do people feel about recent events in the Middle East? Is the rhetoric from a particular opposition group intensifying? What is the range of opinions being expressed in the world press about the best course of action in Iraq? A system that could automatically identify opinions and emotions from text would be an enormous help to someone trying to answer these kinds of questions. Researchers from many subareas of Artificial Intelligence and Natural Language Processing have been working on the automatic identification of opinions and related tasks. To date, most such work has focused on sentiment or subjectivity classification at the document or sentence level. Document classification tasks include, for example, distinguishing editorials from news articles and classifying reviews as positive or negative. A common sentence-level task is to classify sentences as subjective or objective. This paper presents a new approach to phrase-level sentiment analysis that first determines whether an expression is neutral or polar and then disambiguates the polarity of the polar expressions. With this approach, the system is able to automatically identify the contextual polarity for a large subset of sentiment expressions, achieving results that are significantly better than baseline.
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