Extracting and Ranking Product Features in Opinion Documents
Lei Zhang, Bing Liu, SukHwan Lim, Eamonn O’Brien-Strain · 2010
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.