research-article

Generating effective label description for label-aware sentiment classification

Authors:

Stefan DietzeAuthors Info & Claims

Volume 213, Issue PC

https://doi.org/10.1016/j.eswa.2022.119194

Published: 01 March 2023 Publication History

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.

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Extensive experiments conducted on four widely used datasets demonstrate the effectiveness of the proposed approach.

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Digital Library

Cited By

Lee HLee Y(2024)User Opinion-Focused Abstractive Summarization Using Explainable Artificial IntelligenceACM Transactions on Intelligent Systems and Technology10.1145/369645615:6(1-20)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3696456
Ji YWu WHu YChen XHu WHe L(2024)Personality-driven experience storage and retrieval for sentiment classificationThe Journal of Supercomputing10.1007/s11227-024-06170-180:13(18627-18651)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06170-1

Index Terms

Generating effective label description for label-aware sentiment classification
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 213, Issue PC

Mar 2023

1402 pages

ISSN:0957-4174

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Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 March 2023

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Cited By

Lee HLee Y(2024)User Opinion-Focused Abstractive Summarization Using Explainable Artificial IntelligenceACM Transactions on Intelligent Systems and Technology10.1145/369645615:6(1-20)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3696456
Ji YWu WHu YChen XHu WHe L(2024)Personality-driven experience storage and retrieval for sentiment classificationThe Journal of Supercomputing10.1007/s11227-024-06170-180:13(18627-18651)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06170-1

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