RECOGNIZING STRONG AND WEAK OPINION CLAUSES

There has been a recent swell of interest in the automatic identification and extraction of opinions and emotions in text. In this paper, we present the first experimental results classifying the intensity of opinions and other types of subjectivity and classifying the subjectivity of deeply nested clauses. We use a wide range of features, including new syntactic features developed for opinion recognition. We vary the learning algorithm and the feature organization to explore the effect this has on the classification task. In 10-fold cross-validation experiments using support vector regression, we achieve improvements in mean-squared error over baseline ranging from 49% to 51%. Using boosting, we achieve improvements in accuracy ranging from 23% to 96%. In the past few years, interest in the automatic identification and extraction of attitudes, opinions, and sentiments in text has been growing rapidly. This task is motivated by the desire to provide tools and support for information analysts in government, commercial, and political domains, who want to automatically track attitudes and feelings in the news and online forums. How do people feel about recent events in the Middle East? Is the rhetoric from a particular opposition group intensifying? Is there a change in the attitudes being expressed toward the war in Iraq? A system that could automatically identify and extract opinions and emotions from text would be an enormous help to someone trying to answer these kinds of questions. To date, the majority of work on subjectivity and sentiment analysis has focused on 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. However, for many applications, simply recognizing which documents or sentences are opinionated may not be sufficient. Opinions vary in their intensity, and many applications would benefit from being able to determine not only if an opinion is being presented, but how strong is the opinion. Flame detection systems, for example, seek to identify strong rants and emotional tirades, while letting milder opinions pass through. Information analysts need tools that will help them to recognize changes over time in the virulence expressed by persons or groups of interest, and to detect when rhetoric is heating up, or cooling down. A further challenge with automatic opinion identification is that it is not uncommon to find two or more opinions in a single sentence, or to find a sentence containing opinions as well as factual information. Information extraction (IE) systems are natural language processing (NLP) systems that extract from text any information relevant to a prespecified topic. An IE system trying to distinguish between factual information (which should be extracted) and non-factual information (which should be discarded or labeled uncertain) would benefit from the ability to pinpoint the particular clauses that contain opinions. This ability would also be important for multi-perspective question answering systems, which aim to present multiple answers to non-factual questions based on opinions derived from different sources, and for