Paper Info
Title
Multi-task Fuzzy Clustering–Based Multi-task TSK Fuzzy System for Text Sentiment Classification
Abstract
AbstractText 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.
Year
DOI
Venue
2022
10.1145/3476103
ACM Transactions on Asian and Low-Resource Language Information Processing
Keywords
DocType
Volume
Common fuzzy rules, individual fuzzy rules, multi-task fuzzy c-means, multi-task Takagi-Sugeno-Kang fuzzy systems, text sentiment classification
Journal
21
Issue
ISSN
Citations 
2
2375-4699
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Xiaoqing Gu100.34
Kaijian Xia200.34
Yizhang Jiang338227.24
Alireza Jolfaei400.34