Generating effective label description for label-aware sentiment classification. (1st March 2023)

• Record Type: Journal Article
• Authors: Zhu, Xiaofei; Peng, Zhanwang; Guo, Jiafeng; Dietze, Stefan
• Is Part Of: Expert systems with applications. Volume 213:Part C(2023)
• Publication Date: 2023-03-01
• Subjects: Sentiment classification -- Text summarization -- Attention network -- Sentiment analysis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.119194
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Sentiment classification aims to predict the sentiment label for a given text. Recently, several research efforts have been devoted to incorporate matching clues between text words and class labels into the learning process of text representation. However, these methods heavily rely on the availability of label content. Moreover, they simply capture the label-specific signals to measure each word's contribution by either implicitly employing a learnable label representation or explicitly leveraging the interaction between text words and labels via the interaction mechanism. To deal with these issues, in this paper, we propose a novel framework called Label-Guided Dual-view Sentiment Classifier (LGDSC). We first introduce a new strategy for generating an effective label description and then design a novel Dual-Channel Label-guided Attention Network (DLAN) to learn a text representation via two different channels. DLAN will be further leveraged to learn label-guided text representations from two different views. Extensive experimental results on four real-world datasets demonstrate that LGDSC consistently outperforms the state-of-the-art baseline methods. Highlights: Propose an inverse label entropy based strategy for generating effective label descriptions. Design a dual-channel label-guided attention network to learn text representation via two different channels. Extensive experiments conducted on four widely used datasets demonstrate the effectiveness of the proposed approach.