Discovering contextual tags from product review using semantic relatedness

References

• Ahonen-Myka, H., “Finding all maximal frequent sequences in text,” Proceedings of the 16th International conference on Machine Learning ICML-99 Workshop on Machine Learning in Text Data Analysis, J. Stefan Institue, Ljubljana (1999).
   Google Scholar
• Aronson, A. R., O. Bodenreider, et al., “The NLM indexing initiative,” Annual Fall Symposium of the American Medical Informatics Associations, AMIA ’00, American Medical Informatics Association, Los Angeles, CA (2000).
   Google Scholar
• Barker, K. and N. Cornacchia, “Using noun phrase heads to extract document keyphrases,” Advances in Artificial Intelligence, Proceedings 13th Canadian Conference on AI, Montreal, Canada (2000).
   Google Scholar
• Brin, S. and L. Page, “The anatomy of a large-scale hypertextual web search engine,” 7th International Conference World WIde Web, WWW’08, Brisbane, Australia (1998).
   Google Scholar
• Church, K. W. and P. Hanks, “Word association norms, mutual information, and lexicography,” Computational Linguistics, 16(1), 22–29 (1990).
   Google Scholar
• Ding, X. and B. Liu, “The utility of linguistic rules in opinion mining,” Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, Amsterdam, The Netherlands (2007).
   Google Scholar
• Hu, M. and B. Liu, “Mining and summarizing customer reviews,” Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM, Seattle, WA, USA, 168–177 (2004).
   Google Scholar
• Jain, A. K., M. N. Murty, et al., “Data clustering: A review,” ACM Computing Surveys, 31(3), 264–323 (1999).
   Web of Science ®Google Scholar
• Jin, J. and Y. Liu, “How to interpret the helpfulness of online product reviews: bridging the needs between customers and designers,” SMUC ‘10 Proceedings of the 2nd international workshop on search and mining user-generated contents ACM, Toronto, Canada, 87–94 (2010).
   Google Scholar
• Ledeneva, Y., A. Gelbukh, et al., “Terms derived from frequent sequences for extractive text summarization,” in Gelbukh, A. (ed), Computational Linguistics and Intelligent Text Processing, 593–604 (2008).
   Google Scholar
• Lee, T. Y., “Needs-based analysis of online customer reviews,” Proceedings of the ninth international conference on electronic commerce, ACM, Minneapolis, MN, USA, 311–318 (2007).
   Google Scholar
• Ling, X., Q. Mei, et al., “Mining multi-faceted overviews of arbitrary topics in a text collection,” Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Las Vegas, NV, USA, 497–505 (2008).
   Google Scholar
• Liu, M., W. Shen, et al., “An weighted ontology-based semantic similarity algorithm for web service,” Expert Systems with Applications, 36(10), 12480–12490 (2009).
   Google Scholar
• Liu, Y., “Beyond opinion mining: How can automatic online opinion analysis help in product design?,” 6th International Conference on Web Information Systems and Technologies (WEBIST) Valencia, Spain (2010).
   Google Scholar
• Liu, Y., H. T. Loh, et al., “Deriving taxonomy from documents at sentence level,” in Prado, H. A. d. and E. Ferneda (eds), Emerging Technologies of Text Mining: Techniques and Applications, Idea Group, Hershey, PA, 99–119 (2007).
   Google Scholar
• Loh, H. T., J. Sun, et al., “Opinion extraction from customer reviews,” Proceedings of the ASME International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2009), Aug. 30-Sep. 2, ASME, San Diego, CA, USA (2009).
   Google Scholar
• Markó, K., U. Hahn, et al., “Interlingual indexing across different languages,” International Conference Recherche d’Information Assistée par Ordinateur, RIAO’04, Avignon, France (2004).
   Google Scholar
• Medelyan, O., E. Frank, et al., “Human-competitive tagging using automatic keyphrase extraction,” 2009 Conference on Empirical Methods in Natural Language Processing, Aug. 6-7, Singapore (2009).
   Google Scholar
• Mei, Q., D. Xin, et al., “Semantic annotation of frequent patterns,” ACM Transactions on Knowledge Discovery from Data, 1(3), 11–30 (2007).
   Google Scholar
• Mihalcea, R. and P. Tarau, “TextRank–bringing order into texts,” Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, 4 (2004).
   Google Scholar
• Miller, G. A., “WordNet: A lexical database for English,” Communications of the ACM, 38, 39–41 (1995).
   Web of Science ®Google Scholar
• Murphy, M. L., Semantic relations and the Lexicon, Cambridge University Press, Cambridge (2003).
   Google Scholar
• Nguyen, T. D. and M.-Y. Kan, “Keyphrase extraction in scientific publications,” International Conference on Asian Digital Libraries, ICADL’07, Hanoi, Vietnam (2007).
   Google Scholar
• Pasquier, N., Y. Bastide, et al., “Discovering frequent closed itemsets for association rules,” 7th International Conference on Database Theory, 398–416 (1999).
   Google Scholar
• Porter, M. F., “An algorithm for suffix stripping,” Program: Electronic Library and Information Systems, 14(3), 130–137 (1980).
   Google Scholar
• Resnik, P., “Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language,” Journal of Artificial Intelligence Research, 11(1), 95–130 (1999).
   Google Scholar
• Turney, P. D., “Learning algorithms for keyphrase extraction,” Information Retrieval, 2(4), 303–336 (2000).
   Google Scholar
• Tversky, A., “Features of similarity,” Psychological Review, 84(4), 327–352 (1977).
   Web of Science ®Google Scholar
• Witten, I. H., G. W. Paynter, et al., “KEA: Practical automatic keyphrase extraction,” Proceedings of the Fourth ACM Conference on Digital Libraries, DL’99, ACM, Berkeley, CA, USA (1999).
   Google Scholar
• Wu, Y.-f. B., Q. Li, et al., “Finding nuggets in documents: A machine learning approach,” Journal of the American Society for Information Science and Technology, 57(6), 740–752 (2006).
   Google Scholar
• Ye, S., T.-S. Chua, et al., “Document concept lattice for text understanding and summarization,” Information Processing & Management, 43(6), 1643–1662 (2007).
   Google Scholar
• Zhan, J., H. T. Loh, et al., “Gather customer concerns from online product reviews – A text summarization approach,” Expert Systems with Applications, 36(2), 2107–2115 (2009).
   Google Scholar