Generating Recommendation Status of Electronic Products from Online Reviews
ABSTRACT
The need for effective and efficient mining of online reviews cannot be overemphasized. This position is as a result of the overwhelmingly large number of reviews available online which makes it cumbersome for customers to read through all of them. Hence, the need for online web review mining system which will help customers as well as manufacturers read through a large number of reviews and provide a quick description and summary of the performance of the product. This will assist the customer make better and quick decision, and also help manufacturers improve their products and services. This paper describes a research work that focuses on mining the opinions expressed on some electronic products, providing ranks or ratings for the features, with the aim of summarizing them and making recommendations to potential customers for better online shopping. A technique is also proposed for scoring segments with infrequent features. The evaluation results using laptops demonstrate the effectiveness of these techniques.
KEYWORDS
Opinion Aggregation; Electronic Commerce; Infrequent Feature; Electronic Product; Recommendation
Opinion Aggregation; Electronic Commerce; Infrequent Feature; Electronic Product; Recommendation
Cite this paper
B. Ojokoh, O. Olayemi and O. Adewale, "Generating Recommendation Status of Electronic Products from Online Reviews," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 1-10. doi: 10.4236/ica.2013.41001.
B. Ojokoh, O. Olayemi and O. Adewale, "Generating Recommendation Status of Electronic Products from Online Reviews," Intelligent Control and Automation, Vol. 4 No. 1, 2013, pp. 1-10. doi: 10.4236/ica.2013.41001.
References
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[1] L. Zhang, B. Liu, S. H. Lim, S. H. and E. O’Brien-Strain, “Extracting and Ranking Product Features in Opinion Documents,” Proceedings of the 23rd International Conference on Computational Linguistics (COLING), Beijing, 23-27 August 2010, pp. 1462-1470. [2] S. Aciar, “Mining Context Information from Consumer’s Reviews,” Proceedings of the 2nd Workshop on Contex-Aware Recommender Systems, Barcelona, 26 September 2010. [3] X. Ding, B. Liu and P. S. Yu, “A Holistic Lexicon-Based Approach to Opinion Mining,” Proceedings of WSDM 08, California, Palo Alto, 11-12 February 2008, pp. 231-239. doi:10.1145/1341531.1341561 [4] G. Somprasertsri and P. Lalitrojwong, “Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization,” Journal of Universal Computer Science, Vol. 16, No. 6, 2009, pp. 938-955. [5] X. Meng and H. Wang, “Mining User Reviews: From Specification to Summarization,” Proceedings of the ACLIJCNLP 2009 Conference Short Papers, 2009, pp. 177-180. doi:10.3115/1667583.1667637 [6] W. Jin and H. H. Ho, “A Novel Lexicalized HMM-Based Learning Framework for Web Opinion Mining,” Proceedings of the 26th International Conference on Machine Learning, Montreal, 14-18 June 2009. [7] M. Hu and B. Liu, “Mining and Summarizing Customer Reviews,” Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, 22-25 August 2004, pp. 168-177. [8] V. Hatzivassiloglou and K. McKeown, “Predicting the Semantic Orientation of Adjectives,” Proceedings of the 35th Annual Meeting of the Association for Computational Lingusitics, Madrid, 7-10 July 1997, pp. 174-181. doi:10.3115/976909.979640 [9] B. Liu, M. Hu and J. Cheng, “Opinion Observer: Analyzing and Comparing Opinions on the Web. Proceedings of International World Wide Web Conference (WWW’05), New York, 2005, pp. 342-351. doi:10.1145/1060745.1060797 [10] J. Wiebe and E. Riloff, “Creating Subjective and Objective sentence classifiers from unannotated texts,” Proceedings of International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’05), Mexico City, 13-19 February 2005, pp. 486-497. doi:10.1007/978-3-540-30586-6_53 [11] G. Carenini, R. T. Ng and E. Zwart, “Extracting Knowledge from Evaluative Text,” Proceedings of the Third International Conference on Knowledge Capture, Banff, 2-5 October 2005, pp. 11-18. doi:10.1145/1088622.1088626 [12] M. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews,” Proceedings of the Conference on Empirical Methods in Natural Language Processing EMNLP ‘05, Vancouver, 6-8 October 2005, pp. 339-346. doi:10.3115/1220575.1220618 [13] G. Qiu, B. Liu, J. Bu and C. Chen, “Expanding Domain Sentiment Lexicon through Double Propagation,” International Joint Conferences on Artificial Intelligence, Pasadena, 11-17 July 2009. [14] G. Ganapathibhotla and B. Liu, “Identifying Preferred Entities in Comparative Sentences,” Proceedings of the 22nd International Conference on Computational Linguistics (COLING’08), Manchester, 2008. [15] S. Morinaga, K. Yamanishi, K. Tateishi and T. Fukushima, “Mining Product Reputations on the Web,” Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, 23-26 July 2002. [16] S. Feng, M. Zhang and Y. Zhang, “Recommended or Not Recommended? Review Classification through Opinion Extraction,” Proceedings of APWEB Conference, Busan, 6-8 April 2010. [17] B. A. Ojokoh and O. Kayode, “A Feature-Opinion Extraction Approach to Opinion Mining,” Journal of Web Engineering, Vol. 11, No. 1, 2012, pp. 051-063. [18] http://reviewcenter.com [19] Stanford Parser. http://nlp.stanford.edu/software/lex-parser.shtml [20] P. D. Turney, “Thumbs up or Thumbs down? Semantic Orientation Applied to Unsupervised Classification of Reviews,” Proceedings of the Conference of the Association of Computational Lingustics, Howard, 24 August-1 September 2002, pp. 417-424. [21] J. Wiebe, R. Bruce and T. O’Hara, “Development and Use of a Gold Standard Data Set for Subjectivity Classifications,” Proceedings of the Conference of the Association of Computational Lingustics, College Park, 20-26 June 1999. [22] R. Bruce and J. Wiebe, “Recognizing Subjectivity: A Case Study of Manual Tagging,” Natural Language Engineering, Vol. 5, No. 2, 2000 pp. 187-205. doi:10.1017/S1351324999002181 [23] Amazon Web Service. http://www.aws.amazon.com