Classifying web review opinions for consumer product analysis

  • Authors:

  • Christopher C. Yang;Y. C. Wong;Chih-Ping Wei

  • Affiliations:

  • Drexel University, Philadelphia, PA;The Chinese University of Hong Kong, Shatin, Hong Kong;National Tsing Hua University, Hsinchu, Taiwan

  • Venue:
  • Proceedings of the 11th International Conference on Electronic Commerce
  • Year:
  • 2009

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Abstract

Web 2.0 technologies have facilitated the interaction between web users. The business-to-consumer electronic commerce is no longer restricted between consumers and online retail stores. It has been extended to opinions sharing between consumers and consumers. Before making purchasing decision, consumers like to see other consumer opinions in order to identify the best consumer product that fits their preferences. In the recent years, many research works have focused on sentiment classification and analysis of online consumer reviews. Most of them rely on natural language processing techniques to parse and analyze the sentences in online consumer reviews. However, the writing of consumer reviews contributed by web users is usually less formal than those appear in news or journal articles. Many sentences in consumer reviews may contain grammatical errors and unknown terms that do not exist in any dictionaries. As a result, the natural language processing rules are not applicable in many consumer review text and the performance is relatively poor. In this work, we propose to utilize machine learning techniques to classify the consumer product features and produce a summary of consumer reviews for products such as digital cameras. We have conducted an experiment to compare the performance of class association rules and naïve Bayesian classifier for sentiment analysis. The results show that over 70% of macro- and micro- F measures are achieved. It is substantially higher than those achieved by natural language processing approaches.