The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%. 1. Introduction The Internet offers an effective, global platform for E-commerce, communication, and opinion sharing. It has several blogs devoted to diverse topics like finance, politics, travel, education, sports, entertainment, news, history, environment, and so forth. on which people frequently express their opinions in natural language. Mining through these terabytes of user review data is a challenging knowledge-engineering task. However, automatic opinion mining has several useful applications. Hence, in recent years researchers have proposed approaches for mining user-expressed opinions from several domains such as movie reviews [1], political debates [2], restaurant food reviews [3], and product reviews [4–11], and so forth Generating user-query specific summaries is also an interesting application of opinion mining [12, 13]. Our focus in this paper is efficient feature extraction, sentiment polarity classification, and comparative feature summary generation of online product reviews. Nowadays, several websites are available on which a variety of products are advertised and sold. Prior to making a purchase an online shopper typically browses through several similar products of different brands before reaching a final decision. This seemingly simple information retrieval task actually involves a lot of feature-wise comparison and decision making, especially since all manufacturers advertise similar features and competitive prices for most products. However, most online shopping sites also allow
References
[1]
L. Zhao and C. Li, “Ontology based opinion mining for movie reviews,” in Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management, pp. 204–214, 2009.
[2]
A. Balahur, Z. Kozareva, and A. Montoyo, “Determining the polarity and source of opinions expressed in political debates,” in Proceedings of the 10th International Conference on Intelligent Text Processing and Computational Linguistics, vol. 5449 of Lecture Notes in Computer Science, pp. 468–480, Springer, 2009.
[3]
Y. H. Gu and S. J. Yoo, “Mining popular menu items of a restaurant from web reviews,” in Proceedings of the International Conference on Web Information Systems and Mining (WISM '11), vol. 6988 of Lecture Notes in Computer Science, pp. 242–250, Springer, 2011.
[4]
M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘04), pp. 168–177, August 2004.
[5]
M. A. Jahiruddin, M. N. Doja, and T. Ahmad, “Feature and opinion mining for customer review summarization,” in Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence (PReMI ‘09), vol. 5909 of Lecture Notes in Computer Science, pp. 219–224, 2009.
[6]
S. Shi and Y. Wang, “A product features mining method based on association rules and the degree of property co-occurrence,” in Proceedings of the International Conference on Computer Science and Network Technology (ICCSNT '11), pp. 1190–1194, December 2011.
[7]
S. Huang, X. Liu, X. Peng, and Z. Niu, “Fine-grained product features extraction and categorization in reviews opinion mining,” in Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW '12), pp. 680–686, 2012.
[8]
C.-P. Wei, Y.-M. Chen, C.-S. Yang, and C. C. Yang, “Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews,” Information Systems and e-Business Management, vol. 8, no. 2, pp. 149–167, 2010.
[9]
A.-M. Popescu and O. Etzioni, “Extracting product features and opinions from reviews,” in Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP '05), pp. 339–346, October 2005.
[10]
H. Zhang, Z. Yu, M. Xu, and Y. Shi, “Feature-level sentiment analysis for Chinese product reviews,” in Proceedings of the IEEE 3rd International Conference on Computer Research and Development (ICCRD '11), vol. 2, pp. 135–140, March 2011.
[11]
W. Y. Kim, K. I. Kim, J. S. Ryu, and U. M. Kim, “A method for opinion mining of product reviews using association rules,” in Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (ICIS '09), pp. 270–274, November 2009.
[12]
O. Feiguina and G. Lapalme, “Query-based summarization of customer reviews,” in Proceedings of the 20th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence, vol. 4509 of Lecture Notes in Artificial Intelligence, pp. 452–463, Springer, 2007.
[13]
F. Jin, M. Huang, and X. Zhu, “A query-specific opinion summarization system,” in Proceedings of the 8th IEEE International Conference on Cognitive Informatics (ICCI '09), pp. 428–433, June 2009.
[14]
L. Dey and S. M. Haque, “Opinion mining from noisy text data,” International Journal on Document Analysis and Recognition, vol. 12, no. 3, pp. 205–226, 2009.
[15]
K. W. Church and P. Hanks, “Word association norms, mutual information and lexicography,” Computational Linguistics, vol. 16, no. 1, pp. 22–29, 1990.
[16]
W. Zhang, T. Yoshida, and X. Tang, “Text classification using multi-word features,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC '07), pp. 3519–3524, October 2007.
[17]
M. K. Dalal and M. A. Zaveri, “Automatic text classification of sports blog data,” in Proceedings of the Computing, Communications and Applications Conference (ComComAp '12), pp. 219–222, January 2012.
[18]
W. Zhang, T. Yoshida, and X. Tang, “TFIDF, LSI and multi-word in information retrieval and text categorization,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC '08), pp. 108–113, October 2008.
[19]
M. Hu and B. Liu, “Mining opinion features in customer reviews,” in Proceedings of the 19th National Conference on Artifical Intelligence (AAAI '04), pp. 755–760, San Jose, Calif, USA, July 2004.
[20]
H. P. Luhn, “The automatic creation of literature abstracts,” IBM Journal of Research and Development, vol. 2, pp. 159–165, 1958.
[21]
H. P. Edmundson, “New methods in automatic extracting,” Journal of the ACM, vol. 16, pp. 264–285, 1969.
[22]
C. Y. Lin and E. H. Hovy, “Manual and automatic evaluation of summaries,” in Proceedings of the ACL-02 Workshop on Automatic Summarization, vol. 4, pp. 45–51, 2002.
[23]
C. Y. Lin and E. H. Hovy, “Identifying topics by position,” in Proceedings of the 5th Conference on Applied Natural Language Processing, pp. 283–290, 1997.
[24]
R. Barzilay and M. Elhadad, “Using lexical chains for text summarization,” in Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, pp. 10–17, 1997.
[25]
D. Marcu, “Improving summarization through rhetorical parsing tuning,” in Proceedings of the 6th Workshop on Very Large Corpora, pp. 206–215, 1998.
[26]
B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” in Proceedings of the International Conference on Empirical Methods in Natural Language Processing (EMNLP '02), pp. 79–86, 2002.
[27]
V. Hatzivassiloglou and K. Mckeown, “Predicting the semantic orientation of adjectives,” in Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics (ACL ‘98), pp. 174–181, 1998.
[28]
P. D. Turney, “Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews,” in Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 417–424, 2002.
[29]
R. Agrawal and R. Srikant, “Fast algorithm for mining association rules,” in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499, 1994.
[30]
H. Kanayama and T. Nasukawa, “Fully automatic lexicon expansion for domain-oriented sentiment analysis,” in Proceedings of the 11th Conference on Empirical Methods in Natural Language Proceessing (EMNLP '06), pp. 355–363, July 2006.
[31]
L. Wu, Y. Zhou, F. Tan, F. Yang, and J. Li, “Generating syntactic tree templates for feature-based opinion mining,” in Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA '11), vol. 7121 of Lecture Notes in Artificial Intelligence, pp. 1–12, Springer, 2011.
[32]
G. A. Miller, “WordNet: a lexical database for English,” Communications of the ACM, vol. 38, no. 11, pp. 39–41, 1995.
[33]
S. Baccianella, A. Esuli, and F. Sebastiani, “SentiWordNet 3. 0: an enhanced lexical resource for sentiment analysis and opinion mining,” in Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC '10), pp. 2200–2204, 2010.
[34]
D. Sleator and D. Temperley, “Parsing English with a link grammar,” in Proceedings of the 3rd International Workshop on Parsing Technologies, pp. 1–14, 1993.
[35]
V. N. Huynh, T. B. Ho, and Y. Nakamori, “A parametric representation of linguistic hedges in Zadeh's fuzzy logic,” International Journal of Approximate Reasoning, vol. 30, no. 3, pp. 203–223, 2002.
[36]
T. Zamali, M. A. Lazim, and M. T. A. Osman, “Sensitivity analysis using fuzzy linguistic hedges,” in Proceedings of the IEEE Symposium on Humanities, Science and Engineering Research, pp. 669–672, 2012.
