Potential customers often browse online reviews before buying products. Manufacturers also collect customer feedback from the reviews. It is very hard for customers and manufacturers to get useful information from a large number of comments quickly. Thus, automatic information extraction in reviews has become a significant problem. This thesis investigates feature word extraction. Feature words are product components or attributes indicating customer interests. Since there is no systematic study on feature word extraction, we first study three classic methods: (1) the frequency-based extraction method; (2) the Web PMI-based extraction method; (3) the rapid automatic keyword extraction (RAKE) method. To provide an objective evaluation, the performance of each method is validated and compared from the following aspects: precision and recall, time complexity, and robustness. Then a new approach is proposed, the rapid feature word extraction (RFWE) method, to improve the performance. RFWE combines the techniques used in the popular methods and performs well in precision, recall, and runtime. RFWE is a great option for users to extract feature words from customer reviews.
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