Fine-Grained Emotion Elements Extraction and Tendency Judgment Based on Mixed Model
Abstract
Nowadays, with the development of internet technology and electronic commerce, the Web storages huge number of product reviews comment by customers. Product reviews tend to be more objective in reflecting the real situation of the product, more and more customers post product reviews at merchant websites in order to make an informed choice. However, a large number of reviews made it difficult to track the comments and suggestions that customers made. In this paper, a fine-grained emotional element detection and emotional tendency judgment method based on conditional random fields (CRFs) and support vector machine (SVM) was proposed. This model introduces semantics and word meaning in CRF model to improve the robustness. In SVM model, deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.
References
Hu, Weishu, Z. Gong, and J. Guo. ”Mining Product Features from Online Reviews.” IEEE, International Conference on E-Business Engineering IEEE, 2010:24-29.
Mackiewicz, J, and D. Yeats. ”Product Review Users’ Perceptions of Review Quality: The Role of Credibility, Informativeness, and Readability.” Professional Communication IEEE Transactions on 57.4(2014):309-324.
Hong, Yili, P. Y. Chen, and L. M. Hitt. ”Measuring Product Type With Dynamics of Online Product Review Variance.” Social Science Electronic Publishing (2012).
Agrawal, Rakesh, et al. ”Mining association rules between sets of items in large databases.” ACM SIGMOD International Conference on Management of Data ACM, 1993:207-216.
Popescu, Ana Maria. ”Extracting product features and opinions from reviews.” Hlt/emnlp on Interactive Demonstrations Association for Computational Linguistics, 2005:32-33.
Jin, Wei, and H. H. Ho. ”A novel lexicalized HMM-based learning framework for web opinion mining NOTE FROM ACM: A Joint ACM Conference Committee has determined that the authors of this article violated ACM’s publication policy on simultaneous submissions. Therefore ACM has shut of.” International Conference on Machine Learning ACM, 2009:465-472.
Q Liu, B Ma. Product features and emotion tendency mining[J].Information Technology and Informatization. 2015(12)
Hearst, Marti A. ”Direction-Based Text Interpretation as an Information Access Refinement.” Text-based intelligent systems L. Erlbaum Associates Inc. 1999.
Benyang, L. I., et al. ”Single-label Cascaded Model for Document Sentiment Analysis.” Journal of Chinese Information Processing 26.4(2012):3-158.
Fu, Guohong, and X. Wang. ”Chinese sentence-level sentiment classification based on fuzzy sets.” International Conference on Computational Linguistics: Posters Association for Computational Linguistics, 2010:312-319.
Zheng, Li Juan, H. W. Wang and K. Q. Guo. ”Sentiment Classification of Chinese Online Reviews: A Comparison between Sentences and Paragraphs.” Journal of the China Society for Scientific and Technical Information, 32(4)(2013) 376384
Lafferty, John D., A. Mccallum, and F. C. N. Pereira. ”Conditional Random Fields: Probabilistic Models For Segmenting And Labeling Sequence Data.” 3.2(2001):282–289.
Xu, B., et al. ”Extraction of Opinion Targets Based on Shallow Parsing Features.” Zidonghua Xuebao/acta Automatica Sinica 37.10(2011):1241-1247.
Joachims, Thorsten. Text categorization with Support Vector Machines: Learning with many relevant features. Machine Learning: ECML-98. Springer Berlin Heidelberg, 1998:137-142.
Lou, De Cheng, and T. F. Yao. ”Semantic polarity analysis and opinion mining on Chinese review sentences.” Journal of Computer Applications 26.11(2006):2622-2625.
Mikolov T, Sutskever I, Chen K, et al. Distributed Representations of Words and Phrases and their Compositionality[J]. Advances in Neural Information Processing Systems, 2013, 26:3111-3119.