The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the Log Likelihood Ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.
Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users' review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders.
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