Multi-task Fuzzy Clustering-Based Multi-task TSK Fuzzy System for Text Sentiment Classification

Xiaoqing Gu, Kaijian Xia, Yizhang Jiang, Alireza Jolfaei

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering-based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.

Original languageEnglish
Article number33
Number of pages24
JournalACM Transactions on Asian and Low-Resource Language Information Processing
Issue number2
Publication statusPublished - Mar 2022
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grants 61702225, 61806026, and 62171203, Natural Science Foundation of Jiangsu Province under Grant BK 20211333, and Changzhou Scientific and Technological Support Social Development Project CE20215032. Authors’ addresses: X. Gu, Changzhou University, Gehu Road 21, Changzhou 213164, China; email: [email protected]; K. Xia, Affiliated Changshu Hospital of Soochow University, Shuyuan, Street 1, Changshu 215500, China; email: [email protected]; Y. Jiang (corresponding author), Jiangnan University, Lihu Road 1800, Wuxi 214122, China; email: [email protected]; A. Jolfaei, Macquarie University, Sydney NSW 2109, Australia; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 2375-4699/2021/11-ART33 $15.00

Publisher Copyright:
© 2021 Association for Computing Machinery.


  • Common fuzzy rules
  • individual fuzzy rules
  • multi-task fuzzy c-means
  • multi-task Takagi-Sugeno-Kang fuzzy systems
  • text sentiment classification


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