A Maximum Entropy Model for Product Feature Extraction in Online Customer Reviews

Abstract

Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. In this paper, we propose an approach to product feature extraction using a maximum entropy model. Maximum entropy is a probability distribution estimation technique. It is widely used for classification problems in natural language processing, such as question answering, information extraction, and part-of-speech tagging. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Using a maximum entropy approach, at first we extract features from the corpus, train maximum entropy model with an annotated corpus, and then use it with additional product feature discovery to extract product features from customer reviews. Our experimental results show that this approach can work effectively for product feature extraction with 71.88% precision and 75.23% recall.