We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Review Mining for Feature based Opinion Summarization and Visualization

by Ahmad Kamal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 17
Year of Publication: 2015
Authors: Ahmad Kamal
10.5120/21157-4183

Ahmad Kamal . Review Mining for Feature based Opinion Summarization and Visualization. International Journal of Computer Applications. 119, 17 ( June 2015), 6-13. DOI=10.5120/21157-4183

@article{ 10.5120/21157-4183,
author = { Ahmad Kamal },
title = { Review Mining for Feature based Opinion Summarization and Visualization },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 17 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number17/21157-4183/ },
doi = { 10.5120/21157-4183 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:04:16.816460+05:30
%A Ahmad Kamal
%T Review Mining for Feature based Opinion Summarization and Visualization
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 17
%P 6-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The application and usage of opinion mining, especially for business intelligence, product recommendation, targeted marketing etc. have fascinated many research attentions around the globe. Various research efforts attempted to mine opinions from customer reviews at different levels of granularity, including word-, sentence-, and document-level. However, development of a fully automatic opinion mining and sentiment analysis system is still elusive. Though the development of opinion mining and sentiment analysis systems are getting momentum, most of them attempt to perform document-level sentiment analysis, classifying a review document as positive, negative, or neutral. Such document-level opinion mining approaches fail to provide insight about users' sentiment on individual features of a product or service. Therefore, it seems to be a great help for both customers and manufacturers, if the reviews could be processed at a finer-grained level and presented in a summarized form through some visual means, highlighting individual features of a product and users sentiment expressed over them. In this paper, the design of a unified opinion mining and sentiment analysis framework is presented at the intersection of both machine learning and natural language processing approaches. Also, design of a novel feature-level review summarization scheme is proposed to visualize mined features, opinions and their polarity values in a comprehendible way.

References
  1. Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), pp. 1-167.
  2. Kamal, A. 2013. Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources. International Journal of Computer Science Issues (IJCSI), 10(5), pp. 191-200.
  3. Kamal, A. , Abulaish, M. , & Anwar, T. 2012. Mining Feature-Opinion Pairs and their Reliability Scores from Web Opinion Sources. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, ACM.
  4. Kamal, A. , & Abulaish, M. 2013. Statistical Features Identification for Sentiment Analysis using Machine Learning Techniques. In 2013 International Symposium on Computational and Business Intelligence (ISCBI), IEEE, pp. 178-181.
  5. Riloff, E. and Wiebe, J. 2003. Learning Extraction Patterns for Subjective Expressions. In Proceeding of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP-03), pp. 105-112.
  6. Pang, B. , Lee, L. , & Vaithyanathan, S. 2002. Thumbs up? Sentiment Classification Using Machine Learning Techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86.
  7. Qu, L. , Toprak, C. , Jakob, N. , & Gurevych, I. 2008. Sentence Level Subjectivity and Sentiment Analysis Experiments in NTCIR-7 MOAT Challenge, In: Proceedings of the 7th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering, and Cross-Lingual Information Access, Tokyo, Japan, pp. 210–217.
  8. Zhuang, L. , Jing, F. , & Zhu, X. Y. 2006. Movie Review Mining and Summarization. In Proceedings of the 15th ACM International Conference on Information and Knowledge Management, ACM, pp. 43-50.
  9. Qiu, G. , Liu, B. , Bu, J. , & Chen, C. 2009. Expanding Domain Sentiment Lexicon through Double Propagation. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, San Francisco, CA, USA, pp. 1199–1204.
  10. Qiu, G. , Liu, B. , & Chen, C. 2011. Opinion Word Expansion and Target Extraction through Double Propagation. Association for Computational Linguistics 37, pp. 9–27.
  11. Jakob, N. , & Gurevych, I. 2010. Using Anaphora Resolution to Improve Opinion Target Identification in Movie Reviews. In Proceedings of the ACL 2010 Conference Short Papers, Stroudsburg, Uppsala, Sweden, pp. 263-268.
  12. Kessler, J. S. , & Nicolov, N. 2009. Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, San Jose, CA, USA, pp. 90-97.
  13. Esuli, A. , & Sebastiani, F. 2006. Determining Term Subjectivity and Term Orientation for Opinion Mining, In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics (EACL-2006), pp. 193-200.
  14. Esuli, A. , & Sebastiani, F. 2006. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining, In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC), European Assoc. Language Resources, pp. 417–422.
  15. Turney, P. D. , & Littman, M. L. 2003. Measuring Praise and Criticism: Inference of Semantic Orientation from Association, ACM Transactions on Information Systems, 21(4), pp. 315–346.
  16. Prabowo, R. , & Thelwall, M. 2009. Sentiment Analysis: A Combined Approach, Journal of Informetrics, 3(2), pp. 143–157.
  17. Ku, L. W. , Liang, Y. T. , & Chen, H. H. 2006. Opinion Extraction, Summarization and Tracking in News and Blog Corpora. In Proceedings of the AAAI Symposium on Computational Approaches to Analyzing Weblogs (AAAI-CAAW), pp. 100-107.
  18. Mei, Q. , Ling, X. , Wondra, M. , Su, H. , & Zhai, C. 2007. Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs. In Proceedings of the 16th International Conference on World Wide Web, ACM, pp. 171-180.
  19. Pang, B. , & Lee, L. 2008. Opinion Mining and Sentiment Analysis. Foundation and Trends in Information Retrieval, 2(1-2), pp. 1–135.
  20. Lu, Y. , Zhai, C. , & Sundaresan, N. 2009. Rated Aspect Summarization of Short Comments. In: Proceedings of the 18th International Conference on World Wide Web, ACM, pp. 131-140.
  21. Gamon, M. , Aue, A. , Corston-Oliver, S. , & Ringger, E. 2005. Pulse: Mining Customer Opinions from Free Text. In Proceedings of the International Symposium on Intelligent Data Analysis (IDA), Lecture Notes in Computer Science No. 3646, pp. 121–132.
  22. 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, Japan, pp. 342–351.
  23. Filho, P. B. , Brun, C. , & Rondeau, G. 2012. A Graphical User Interface for Feature-Based Opinion Mining. In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), pp. 5-8.
  24. Friedman, N. , Geiger, D. , & Goldszmidt, M. 1997. Bayesian Network Classifiers, Machine Learning, 29 (2-3), pp. 131-163.
  25. Quinlan, J. R. 1993. C4. 5 Programs for Machine Learning, Morgan Kaufmann Publishers Inc. , San Francisco, CA, USA, ISBN: 1-55860-238-0.
  26. Rumelhart, D. E. , Hinton, G. E. & Williams, R. J. 1986. Learning Internal Representations by Error Propagation, California Univ San Diego La Jolla Inst for Cognitive Science, Accession Number: ADA164453.
  27. Bbeiman, L. 1996. Bagging Predictors, Machine Learning, 24(2), pp. 123-140.
  28. Abulaish, M. , Doja, M. N. , & Ahmad, T. 2009. Feature and opinion mining for customer review summarization. In Pattern Recognition and Machine Intelligence, Springer Berlin Heidelberg, pp. 219-224.
  29. Kleinberg, J. M. 1999. Authoritative Sources in a Hyperlinked Environment, Journal of the ACM (JACM), 46 (5), ACM, pp. 604-632.
  30. Church, K. W. , & Hanks, P. 1990. Word Association Norms, Mutual Information and Lexicography, In: Computational Linguistics, 16 (1), pp. 22-29.
  31. Fano, R. M. 1961. Transmission of Information: A Statistical Theory of Communications, American Journal of Physics, 29, pp. 793-794.
  32. Dunning, T. 1993. Accurate Methods for the Statistics of Surprise and Coincidence, Computational Linguistics, 19(1), pp. 61-74.
  33. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, & Ian H. Witten. 2009. "The WEKA Data Mining Software: An Update" SIGKDD Explorations, 11(1), pp. 10-18.
Index Terms

Computer Science
Information Sciences

Keywords

Opinion Mining Subjectivity Classification Feature Identification Sentiment Classification Natural Language Processing Rule-Based System Machine Learning Review Summarization.

Powered by PhDFocusTM