References: [1] Balahur, A., Hermida, J. M., Montoyo, A. (2012). Detecting implicit expressions of emotion in text: a comparative analysis, Decision Support Systems, 53, 742– 753. [2] Ghose, A., Ipeirotis, P. G., Li, B. B. (2014). Examining the impact of ranking on consumer behavior and search engine revenue, Manag. Sci. 60 (7) 1632–1654. [3] Liu, B., Hu, M., Cheng, J. (2005). Opinion observer: analyzing and comparing opinions on the web, In: Proceedings of International Conference of World Wide Web, Publishing, 2005, p. 342–351. [4] Lin, C., He, Y., Everson, R., Ruger, S. (2012). Weakly supervised joint sentiment-topic detection from text, IEEE Transactions on Knowledge and Data Engineering 24 (6) 1134–1145. [5] Wei, C. P., Chen, Y. M., Yang, C. S., Yang, C. C. (2010). Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews, Information Systems and E-Business Management, 8 (2) 149–167. [6] Eirinaki, M., Pisal, S., Singh, J. (2012). Feature-based opinion mining and ranking. J. Comput. Syst. Sci., 78, 1175–1184. [7] Qiu, G., Liu, B., Bu, J., Chen, C. (2011). Opinion word expansion and target extraction through double propagation, Comput. Linguist. 37 (1) 9–27. [8] Somprasertsri, G., Lalitrojwong, P. (2010). Mining feature- opinion in online customer eviews for opinion summarization, J. Univers. Comput. Sci. 16 (6) 938–955. [9] Wang, G. A., Jiao, J., Abrahams, A. S., Fan, W., Zhang, Z. (2013). Expertrank:, A topic-aware expert finding algorithm for online knowledge communities, Decis. Support Syst. 54 (3) 1442–1451. [10] Golub, G. H., Van Loan, C. F. (2012). Matrix Computations, Johns Hopkins University Press, Baltimore, MD, 2012. [11] Titov, I., McDonald, R. (2008). A joint model of text and aspect ratings for sentiment summarization, in: Proceedings of the Annual Meeting on Association for Computational Linguistics and the Human Language Technology Conference (ACL-HLT), Publishing, p. 308–316. [12] IRESEARCH, China internet shopping market report in 2013, 2014-5, <http://ec.iresearch.cn/shopping/20140114/224908.shtml> [13] Toutanova, K., Klein, D., Manning, C. D., Singer, Y. (2003). Feature-rich part-of-speech tagging with a cyclic dependency network, in: Proceedings of HLT-NAACL, Publishing, p. 252–259. [14] Kang, Y., Zhou, L. (2017). RubE: Rule-based methods for extracting product features from online consumer reviews. Inf. Manag. 54, 166–176. [15] Li, S., Zhou, L., Li, Y. (2015). Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Inf. Process. Manag. 51, 58–67. [16] Long, C., Zhang, J., Zhut, X. (2010). A Review Selection Approach for Accurate Feature Rating Estimation. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Beijing, China, 23–27 August 2010; Association for Computational Linguistics: Stroudsburg, PA, USA, p. 766–774. [17] Aljukhadar, M., Senecal, S., Daoust, C. E. (2012). Using recommendation agents to cope with information overload, Int. J. Electron. Commer. 17 (2) 41–70. [18] Hu, M., Liu, B. (2004). Mining and summarizing customer reviews, in: Proceedings of International Conference on Knowledge Discovery and Data Mining, Publishing, ACM SIGKDD, p. 168–177. [19] Hu, M., Liu, B. (2004). Mining opinion features in customer reviews, In: Proceedings of 19th National Conference on Artificial Intelligence, Publishing, AAAI Press, p. 755–760. [20] Popescu, A. M., Etzioni, O. (2005). OPINE: Extracting Product Features and Opinions from Reviews. In: Proceedings of the HLT/EMNLP on Interactive Demonstrations, Vancouver, BC, Canada, 7 October 2005; Association for Computational Linguistics: Stroudsburg, PA, USA, p. 32–33. [21] Poria, S., Ofek, N., Gelbukh, A., Hussain, A., Rokach, L. (2014). Dependency tree-Based rules for concept-level aspect-based sentiment analysis. In Semantic Web Evaluation Challenge; Springer: Cham, Switzerland, p. 41–47. [22] Su, Q., Xiang, K., Wang, H., Sun, B., Yu, S. (2006). Using pointwise mutual information to identify implicit features in customer reviews, In: Proceedings of 17th International Conference on Computer Processing of Oriental Languages, Publishing, p. 22–30. [23] Qiu, G., Liu, B., Bu, J., Chen, C. (2009). Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence, Pasadena, CA, USA, 11–17 July 2009; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, p. 1199–1204. [24] Lau, R.Y., Li, C., Liao, S. S. (2014). Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis, Decis. Support Syst. 65, p. 80–94. [25] Rana, T. A., Cheah, Y. N. (2017). A Two-Fold Rule- Based Model for Aspect Extraction. Expert Syst. Appl. 89, 273–285. [26] Brin, S., Page, L. (1998). The anatomy of a largescale hypertextual web search engine, Comput. Netw. ISDN Syst. 30 (1) 107–117. [27] Brody, S., Elhadad, N. (2010). An unsupervised aspect- sentiment model for online reviews, In: Proceedings of Annual Conference of the North American Chapter of the Association for Computational Linguistics, Publishing, Association for Computational Linguistics, p. 804– 812. [28] Scaffidi, C., Bierhoff, K., Chang, E., Felker, M., Ng, H., Jin, C. (2007). Red Opal: Product-feature scoring from reviews. In: Proceedings of the 8th ACM Conference on Electronic Commerce, San Diego, CA, USA, 11–15 June 2007; ACM: New York, NY, USA, p. 182–191. [29] Su, S. H., Lynn, K. T. (2013). Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews. Sci. World J. 2013, 394758:1– 394758:5. [30] Thet, T. T., Na, J. C., Khoo, C. S. (2010). Aspectbased sentiment analysis of movie reviews on discussion boards, Journal of Information Science, 36 (6) 823–848. [31] Fan, W. G., Gordon, M. D. (2014). The power of social media analytics, Commun. ACM 57 (6), p. 74–81. [32] Fu, X., Liu, G., Guo, Y., Wang, Z. (2013). Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon, Knowledge Based Systems, 37, 186–195. [33] Wu, Y., Zhang, Q., Huang, X., Wu, L. (2009). Phrase dependency parsing for opinion mining, In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, (Singapore), p. 1533–1541. [34] Chen, Y. -C., Shang, R. -A., Kao, C. -Y. (2009). The effects of information overload on consumers subjective state towards buying decision in the internet shopping environment, Electron. Commer. Res. Appl. 8 (1) 48–58. [35] Yan, Z., Xing, M., Zhang, D., Ma, B. (2015). EXPRS: An extended pagerank method for product feature extraction from online consumer reviews. Inf. Manag. 52, 850– 858. [36] Zhai, Z., Liu, B., Xu, H., Jia, P. (2011). Constrained LDA for grouping product features in opinion mining, In: Proceedings of 15th Pacific–Asia Conference, Advances in Knowledge Discovery and Data Mining, p. 448–459. [37] Zhang, L., Liu, B., Lim, S. H., O Brien-Strain, E. (2010). Extracting and ranking product features in opinion documents. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, Beijing, China, 23–27 August 2010; Association for Computational Linguistics: Stroudsburg, PA, USA, p. 1462–1470. [38] 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, Arlington, VA, USA, 06–11 November 2006; ACM: New York, NY, USA, p. 43–50. [39] Poria, Soujanya., Cambria, Erik., Ku, Lun-Wei., Gui, Chen., Gelbukh, Alexander. (2014). A Rule-Based Approach to Aspect Extraction from Product Reviews. In: Proceedings of the Second Workshop on Natural Language Processing for Social Media, Dublin, Ireland, August p. 28-37. [40] Rana, T. A., Cheah, Y. N. (2015). Hybrid rule-based approach for aspect extraction and categorization from customer reviews. In: Proceedings of 9th International Conference on IT in Asia (CITA), Kota Samarahan, Malaysia, (August) p. 1-5. [41] Ansari, Gunjan., Saxena, Chandni., Ahmad, Tanvir., Doja, M. N. (2020). Aspect Term Extraction using Graphbased Semi-Supervised Learning, In: Proceedings of International Conference on Computational Intelligence and Data Science (ICCIDS 2019), (February) p. 2080-2090. |