Cross-domain sentiment analysis of product reviews by combining lexicon-based and learn-based techniques

Proceedings - 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security and 2015 IEEE 12th International Conference on Embedded Software and Systems, HPCC-CSS-ICESS 2015

Volumn , Issue , 2015, Pages 351-356

  Mao, Kaili ^a   Niu, Jianwei ^a   Wang, Xuejiao ^a   Wang, Lei ^a   Qiu, Meikang ^b  

^a BEIHANG UNIVERSITY   (China)

^b PACE UNIVERSITY   (United States)

Author keywords

Cross domain; Learn based; Lexicon based; Sentiment analysis

Indexed keywords

COMPUTERS; CONSUMER BEHAVIOR; DATA MINING; EMBEDDED SOFTWARE; EMBEDDED SYSTEMS; MARKETING;

CROSS-DOMAIN; LEARN-BASED; LEXICON-BASED; MARKETING STRATEGY; PRODUCT REVIEWS; PURCHASING BEHAVIORS; SENTIMENT ANALYSIS; STATE-OF-THE-ART METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 84961762828     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HPCC-CSS-ICESS.2015.75     Document Type: Conference Paper

References (20)

1. W. A. S. Singapore, "Social, digital & mobile around the world, " 2014.
2. E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis, " IEEE Intelligent Systems, vol. 28, no. 2, pp. 15-21, 2013.
3. M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, "Lexiconbased methods for sentiment analysis, " Computational linguistics, vol. 37, no. 2, pp. 267-307, 2011.
4. L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, "Target-dependent twitter sentiment classification, " in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 2011, pp. 151-160.
5. M. Eirinaki, S. Pisal, and J. Singh, "Feature-based opinion mining and ranking, " Journal of Computer and System Sciences, vol. 78, no. 4, pp. 1175-1184, 2012.
6. A. Duric and F. Song, "Feature selection for sentiment analysis based on content and syntax models, " Decision Support Systems, vol. 53, no. 4, pp. 704-711, 2012.
7. S. Afantenos, V. Karkaletsis, and P. Stamatopoulos, "Summarization from medical documents: A survey, " Artificial intelligence in medicine, vol. 33, no. 2, pp. 157-177, 2005.
8. B. Liu, M. Hu, and J. Cheng, "Opinion observer: Analyzing and comparing opinions on the web, " in Proceedings of the 14th international conference on World Wide Web. ACM, 2005, pp. 342-351.
9. P. Melville, W. Gryc, and R. D. Lawrence, "Sentiment analysis of blogs by combining lexical knowledge with text classification, " in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 1275-1284.
10. A. Abbasi, H. Chen, and A. Salem, "Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums, " ACM Transactions on Information Systems (TOIS), vol. 26, no. 3, p. 12, 2008.
11. M. Thelwall and K. Buckley, "Topic-based sentiment analysis for the social web: The role of mood and issue-related words, " Journal of the American Society for Information Science and Technology, vol. 64, no. 8, pp. 1608-1617, 2013.
12. M. Thelwall, K. Buckley, and G. Paltoglou, "Sentiment strength detection for the social web, " Journal of the American Society for Information Science and Technology, vol. 63, no. 1, pp. 163-173, 2012.
13. A. Athar, "Sentiment analysis of citations using sentence structure-based features, " in Proceedings of the ACL 2011 student session. Association for Computational Linguistics, 2011, pp. 81-87.
14. R. Socher, J. Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning, "Semi-supervised recursive autoencoders for predicting sentiment distributions, " in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2011, pp. 151-161.
15. M. Ghiassi, J. Skinner, and D. Zimbra, "Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network, " Expert Systems with applications, vol. 40, no. 16, pp. 6266-6282, 2013.
16. C. Lin, Y. He, R. Everson, and S. Ruger, "Weakly supervised joint sentiment-topic detection from text, " Knowledge and Data Engineering, IEEE Transactions on, vol. 24, no. 6, pp. 1134-1145, 2012.
17. W. Du and S. Tan, "Optimizing modularity to identify semantic orientation of chinese words, " Expert Systems with Applications, vol. 37, no. 7, pp. 5094-5100, 2010.
18. S. Tan and J. Zhang, "An empirical study of sentiment analysis for chinese documents, " Expert Systems with Applications, vol. 34, no. 4, pp. 2622-2629, 2008.
19. S. Tan, Y. Wang, and X. Cheng, "Combining learn-based and lexiconbased techniques for sentiment detection without using labeled examples, " in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2008, pp. 743-744.
20. D. Tang, "Research on domain adaptive chinese sentiment lexicon construction, " Ph. D. dissertation, Harbin: Harbin Institute of Technology, 2012.