Cost-Sensitive Text Sentiment Analysis Based on Sequential Three-Way Decision

被引：0
|
作者：
Fan Q. [1 ]
Liu D. [1 ]
Ye X. [1 ]
机构：
[1] School of Economics and Management, Southwest Jiaotong University, Chengdu
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2020年 / 33卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Cost-Sensitive; Granular Computing; Sentiment Analysis; Text Mining; Three-Way Decision;
D O I：
10.16451/j.cnki.issn1003-6059.202008007
中图分类号：
学科分类号：
摘要：
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. © 2020, Science Press. All right reserved.
引用
收藏
页码：732 / 742
页数：10
相关论文
共 36 条
  • [1] ZHAO J, LIU K, XU L H., Sentiment Analysis: Mining Opinions, Sentiments, and Emotions, Computational Linguistics, 42, 3, pp. 595-598, (2016)
  • [2] MEDHAT W, HASSAN A, KORASHY H., Sentiment Analysis Algorithms and Applications: A Survey, Ain Shams Engineering Journal, 5, 4, pp. 1093-1113, (2014)
  • [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)
  • [4] ARCHAK N, GHOSE A, IPEIROTIS P G., Deriving the Pricing Power of Product Features by Mining Consumer Reviews, Management Science, 57, 8, pp. 1485-1509, (2011)
  • [5] SONG S Y, WANG C, CHEN C L, Et al., Sentiment Analysis for Intelligent Customer Service Chatbots, Journal of Chinese Information Processing, 34, 2, pp. 80-95, (2020)
  • [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, 9, 7, pp. 1448-1452, (2019)
  • [7] RANA T A, CHEAH Y N., Aspect Extraction in Sentiment Analysis: Comparative Analysis and Survey, Artificial Intelligence Review, 46, 4, pp. 459-483, (2016)
  • [8] SOMPRASERTSRI G, LALITROJWONG P., Mining Feature-Opi-nion in Online Customer Reviews for Opinion Summarization, Journal of Universal Computer Science, 16, 6, pp. 938-955, (2010)
  • [9] PANG B, LEE L, VAITHYANATHAN S., Thumbs up? Sentiment Classification Using Machine Learning Techniques
  • [10] MOREO A, ROMERO M, CASTRO J L, Et al., Lexicon-Based Comments-Oriented News Sentiment Analyzer System, Expert Systems with Applications, 39, 10, pp. 9166-9180, (2012)
1234