MINING CONSUMER OPINIONS FROM THE WEB

Christopher C. Yang, Y. C. Wong

Abstract

The Web has provided an excellent platform for business to consumer (B2C) electronic commerce. B2C electronic commerce offers convenience, choice, lower cost and customization to consumers.The electronic shopping platform allows consumers to make intelligent comparison and purchasing decision on consumer products. In addition to comparing product specifications as described on electronic catalogue for better purchasing decision, consumers also hunger for consumer reviews to identify the best products that fit their preferences. For example, a professional photographer would like to identify a camera with lens of high quality and zooming power but a general user may like to find a camera that is cheap, light, and with a large LCD screen. When consumers take consumer reviews as reference, they are interested in both opinion orientation and product features that they are describing. Most of the prior works on consumer opinions mining focus on identifying opinion orientation. Some recent works have started to classify product features but heavily rely on linguistic and natural language processing techniques. However, the writing in consumer reviews is usually less formal and many of them do not conform to the grammatical rules. Therefore, the linguistic and language processing approach is not satisfactory. In this work, we propose a sentiment analysis system to classify product features of consumer reviews by mining class association rules. The experimental result shows that the performance is promising. The content mining approach outperforms the natural language processing approach.

References

  1. Bhargava, H. K., Choudhary, V., Krishnan, R., 2000. Pricing and Product Design: Intermediary Strategies in an Electronic Market. In International Journal of Electronic Commerce, Vol. 5, No. 1, pp.37-56.
  2. Das, S., Chen, M., 2001. Yahoo! for Amazon: Extracting Market Sentiment from Stock Message Boards. In Proceedings of the 8th Asia Pacific Finance Association (APFA) Annual Meeting.
  3. Hatzivassiloglou, V., Wiebe, J., 2000. Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In Proceedings of 18th International Conference on Computational Linguistics (COLING), Saarbr├╝cken, Germany, pp.299-305.
  4. Hu, M., Liu, B., 2004. Mining and Summarizing Customer Reviews. In Proceedings of the 10th ACM Conference on Knowledge Discovery and Data Mining (KDD), Seattle, WA, pp.168-177.
  5. Liu, B., Hu, M., Cheng, J., 2005. Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th International Conference on World Wide Web (WWW'05), Chiba, Japan, pp.342- 351
  6. Menczer, F., Street, W.N., Monge, A. E., Adaptive Assistants for Customized E-shopping. In IEEE Intelligent Systems, Vol. 17, No. 6, pp. 12-19.
  7. Pang, B., Lee, L., Vaithyanathan, S., 2002. Thumbs Up? Sentiment Classification Using Machine Learning Techniques. In Proceedings of 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp.79-86.
  8. Turney, P., 2002. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In Proceedings of the 40th Conference on Association for Computational Linguistics (ACL), Philadelphia, PA, pp.417-424.
  9. Wiebe, J., Bruce, R., O'Hara, T., 1999. Development and Use of a Gold Standard Data Set for Subjectivity Classifications. In Proceedings of the 37th Conference on Association for Computational Linguistics (ACL), College Park, MD, pp.246-253
  10. Wei, C., Yang, C. S., Huang, C. N., 2006. Turning Online Product Reviews to Customer Knowledge: A Semantic-based Sentiment Classification Approach. In Proceedings of 10th Pacific Asia Conference on Information Systems (PACIS), Kuala Lumpur, Malaysia.
  11. Wong, R., Yang, C. C., 2005. Collaborative Infomediary for Financial News. In Proceedings of the Fourth Workshop on e-Business (WEB 2005), Las Vegas, NV, December 2005.
  12. Yang, C. C., Wong, R., 2006. Measuring Success Factors of E-Commerce Infomediary. In Proceedings of the Pacific Asia Conference on Information Systems (PACIS), Kuala Lumpur, Malaysia, July 2006.
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Paper Citation


in Harvard Style

C. Yang C. and C. Wong Y. (2008). MINING CONSUMER OPINIONS FROM THE WEB . In Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-8111-27-2, pages 187-192. DOI: 10.5220/0001523201870192


in Bibtex Style

@conference{webist08,
author={Christopher C. Yang and Y. C. Wong},
title={MINING CONSUMER OPINIONS FROM THE WEB},
booktitle={Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2008},
pages={187-192},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001523201870192},
isbn={978-989-8111-27-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - MINING CONSUMER OPINIONS FROM THE WEB
SN - 978-989-8111-27-2
AU - C. Yang C.
AU - C. Wong Y.
PY - 2008
SP - 187
EP - 192
DO - 10.5220/0001523201870192