Abstract：To solve the problems of cost imbalance in text sentiment analysis and high classification cost in static decision-making, a cost-sensitive text sentiment analysis method is constructed based on sequential three-way decision, and the misclassification cost and learning cost in dynamic decision-making process are taken into account. Firstly, a granulation model for text data is proposed to construct a multi-level granular structure. Next, sequential three-way decision is introduced to set a dynamic text analysis framework. Finally, real text review datasets are utilized to validate the effectiveness of the proposed method. Experimental results show that the proposed method significantly reduces the overall decision-making cost with the improved classification quality.
范琴, 刘盾, 叶晓庆. 基于序贯三支决策的代价敏感文本情感分析方法[J]. 模式识别与人工智能, 2020, 33(8): 732-742.
FAN Qin, LIU Dun, YE Xiaoqing. Cost-Sensitive Text Sentiment Analysis Based on Sequential Three-Way Decision. , 2020, 33(8): 732-742.
[1] ZHAO J, LIU K, XU L H. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Computational Linguistics, 2016, 42(3): 595-598. [2] MEDHAT W, HASSAN A, KORASHY H. Sentiment Analysis Algorithms and Applications: A Survey. Ain Shams Engineering Journal, 2014, 5(4): 1093-1113. [3] YI S S, LIU X F. Machine Learning Based Customer Sentiment Analysis for Recommending Shoppers, Shops Based on Customers′ Review. Complex and Intelligent Systems, 2020. DOI: 10.1007/s40747-020-00155-2. [4] ARCHAK N, GHOSE A, IPEIROTIS P G. Deriving the Pricing Power of Product Features by Mining Consumer Reviews. Management Science, 2011, 57(8): 1485-1509. [5] 宋双永,王 超,陈成龙,等.面向智能客服系统的情感分析技术.中文信息学报, 2020, 34(2): 80-95. (SONG S Y, WANG C, CHEN C L, et al. Sentiment Analysis for Intelligent Customer Service Chatbots. Journal of Chinese Information Processing, 2020, 34(2): 80-95.) [6] ZHAN Q Y, ZHUO W, HU W, et al. Opinion Mining in Online Social Media for Public Health Campaigns. Journal of Medical Imaging and Health Informatics, 2019, 9(7): 1448-1452. [7] RANA T A, CHEAH Y N. Aspect Extraction in Sentiment Analysis: Comparative Analysis and Survey. Artificial Intelligence Review, 2016, 46(4): 459-483. [8] SOMPRASERTSRI G, LALITROJWONG P. Mining Feature-Opi-nion in Online Customer Reviews for Opinion Summarization. Journal of Universal Computer Science, 2010, 16(6): 938-955. [9] PANG B, LEE L, VAITHYANATHAN S. Thumbs up? Sentiment Classification Using Machine Learning Techniques[C/OL]. [2020-04-28]. https://arxiv.org/pdf/cs/0205070.pdf. [10] MOREO A, ROMERO M, CASTRO J L, et al. Lexicon-Based Comments-Oriented News Sentiment Analyzer System. Expert Systems with Applications, 2012, 39(10): 9166-9180. [11] KIM S M, HOVY E. Identifying and Analyzing Judgment Opinions // Proc of the Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics. Stroudsburg, USA: ACL, 2006: 200-207. [12] HU M Q, LIU B. Mining and Summarizing Customer Reviews // Proc of the 10th ACM SIGKDD International Conference on Know-ledge Discovery and Data Mining. New York, USA: ACM, 2004: 168-177. [13] 朱嫣岚,闵 锦,周雅倩,等.基于HowNet的词汇语义倾向计算.中文信息学报, 2006, 20(1): 14-20. (ZHU Y L, MIN J, ZHOU Y Q, et al. Semantic Orientation Computing Based on HowNet. Journal of Chinese Information Proce-ssing. 2006, 20(1): 14-20.) [14] CATAL C, GULDAN S. Product Review Management Software Based on Multiple Classifiers. IET Software, 2017, 11(3): 89-92. [15] SRIVASTAVA A, SINGH V, DRALL G S. Sentiment Analysis of Twitter Data: A Hybrid Approach. International Journal of Healthcare Information Systems and Informatics, 2019, 14(2): 1-16. [16] CAI Y, YANG K, HUANG D P, et al. A Hybrid Model for Opi-nion Mining Based on Domain Sentiment Dictionary. International Journal of Machine Learning and Cybernetics, 2019, 10: 2131-2142. [17] PARIMALA M, PRIYA R M S, REDDY M P K, et al. Spatiotemporal-Based Sentiment Analysis on Tweets for Risk Assessment of Event Using Deep Learning Approach[C/OL]. [2020-04-28]. https://onlinelibrary.wiley.com/doi/full/10.1002/spe.2851. [18] KIM Y. Convolutional Neural Networks for Sentence Classification[C/OL]. [2020-04-28]. https://arxiv.org/pdf/1408.5882.pdf. [19] TANG D Y, QIN B, LIU T. Document Modeling with Gated Recurrent Neural Network for Sentiment Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2015: 1422-1432. [20] SADHASIVAM J, BABU R. Sentiment Analysis of Amazon Pro-ducts Using Ensemble Machine Learning Algorithm. International Journal of Mathematical Engineering and Management Sciences, 2019, 4(2): 508-520. [21] ZHOU J, HUANG J X, HU Q V, et al. Is Position Important? Deep Multi-task Learning for Aspect-Based Sentiment Analysis[C/OL]. [2020-04-28]. https://link.springer.com/article/10.1007/s10489-020-01760-x. [22] AVINASH M, SIVASANKAR E. Efficient Feature Selection Techniques for Sentiment Analysis. Multimedia Tools and Applications, 2020, 79(9): 6313-6335. [23] PECENKA C, DEBELLUT F, BAR-ZEEV N, et al. Cost-Effectiveness Analysis for Rotavirus Vaccine Decision-Making: How Can We Best Inform Evolving and Complex Choices in Vaccine Product Selection? Vaccine, 2020, 38(6): 1277-1279. [24] HANSEN K. Decision-Making Based on Energy Costs: Comparing Levelized Cost of Energy and Energy System Costs. Energy Strategy Reviews, 2019, 24: 68-82. [25] LI H X, ZHANG L B, HUANG B, et al. Sequential Three-Way Decision and Granulation for Cost-Sensitive Face Recognition. Knowledge-Based Systems, 2016, 91: 241-251. [26] LIU D, YE X Q. A Matrix Factorization Based Dynamic Granularity Recommendation with Three-Way Decisions. Knowledge-Based Systems, 2020, 191. DOI: 10.1016/j.knosys.2019.105243. [27] 张 钹,张 铃.粒计算未来发展方向探讨.重庆邮电大学学报(自然科学版), 2010, 22(5): 538-540. (ZHANG B, ZHANG L. Discussion on Future Development of Granular Computing. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2010, 22(5): 538-540.) [28] YAO Y Y, DENG X F. Sequential Three-Way Decisions with Pro-babilistic Rough Sets // Proc of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing. Wa-shington, USA: IEEE, 2011: 120-125. [29] ZHANG Y B, MIAO D Q, WANG J Q, et al. A Cost-Sensitive Three-Way Combination Technique for Ensemble Learning in Sentiment Classification. International Journal of Approximate Reaso-ning, 2019, 105: 85-97. [30] 张刚强,刘 群,纪良浩.基于序贯三支决策的多粒度情感分类方法.计算机科学, 2018, 45(12): 153-159. (ZHANG G Q, LIU Q, JI L H. Multi-granularity Sentiment Cla-ssification Method Based on Sequential Three-Way Decisions. Computer Science, 2018, 45(12): 153-159.) [31] YAO Y Y. Three-Way Decisions with Probabilistic Rough Sets. Information Sciences, 2010, 180(3): 341-353. [32] YAO Y Y. Granular Computing and Sequential Three-Way Decisions // Proc of the International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2013: 16-27. [33] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003, 3: 993-1022. [34] LEE D D, SEUNG H S. Learning the Parts of Objects by Non-ne-gative Matrix Factorization. Nature, 1999, 401(6755): 788-791. [35] 李郅琴,杜建强,聂 斌,等.特征选择方法综述.计算机工程与应用, 2019, 55(24): 10-19. (LI Z Q, DU J Q, NIE B, et al. Summary of Feature Selection Method. Computer Engineering and Applications, 2019, 55(24): 10-19.) [36] ESLAMI S P, GHASEMAGHAEI M, HASSANEIN K. Which Online Reviews′ Do Consumers Find Most Helpful? A Multi-method Investigation. Decision Support Systems, 2018, 113: 32-42.
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