Extracting and Ranking Product Features in Opinion Documents

An important task of opinion mining is to extract people’s opinions on features of an entity. For example, the sentence, “I love the GPS function of Motorola Droid ” expresses a positive opinion on the “GPS function ” of the Motorola phone. “GPS function ” is the feature. This paper focuses on mining features. Double propagation is a state-of-the-art technique for solving the problem. It works well for medium-size corpora. However, for large and small corpora, it can result in low precision and low recall. To deal with these two problems, two improvements based on part-whole and “no ” patterns are introduced to increase the recall. Then feature ranking is applied to the extracted feature candidates to improve the precision of the top-ranked candidates. We rank feature candidates by feature importance which is determined by two factors: feature relevance and feature frequency. The problem is formulated as a bipartite graph and the well-known web page ranking algorithm HITS is used to find important features and rank them high. Experiments on diverse real-life datasets show promising results.