A Semantic Classification Approach for Online Product Reviews

  • Wang C
  • Lu J
  • Zhang G
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With the fast growth of e-commerce, product reviews on the Web have become an important information source for customers' decision making when they plan to buy products online. As the reviews are often too many for customers to go through, how to automatically classify them into different semantic orientations (i.e. recommend/not recommend) has become a research problem. Different from traditional approaches that treat a review as a whole, our approach performs semantic classifications at the sentence level by realizing reviews often contain mixed feelings or opinions. In this approach, a typical feature selection method based on sentence tagging is employed and a naive Bayes classifier is used to create a base classification model, which is then combined with certain heuristic rules for review sentence classification. Experiments show that this approach achieves better results than using general naive Bayes classifiers.

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  • Chao Wang

  • Jie Lu

  • Guangquan Zhang

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