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Open Access

Deep Broad Learning for Emotion Classification in Textual Conversations

Center for Linguistics and Applied Linguistics, and Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510006, China
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, South China Normal University, Guangzhou 510006, China
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
Modern Education Technology Center, Guangdong University of Foreign Studies, Guangzhou 510006, China
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Abstract

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations. It is becoming one of the most important tasks for natural language processing in recent years. However, it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context. To address the challenge, we propose a method to classify emotion in textual conversations, by integrating the advantages of deep learning and broad learning, namely DBL. It aims to provide a more effective solution to capture local contextual information (i.e., utterance-level) in an utterance, as well as global contextual information (i.e., speaker-level) in a conversation, based on Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and broad learning. Extensive experiments have been conducted on three public textual conversation datasets, which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification. In addition, the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

Keywords

Convolutional Neural Network (CNN)emotion classificationbroad learningtextual conversationBidirectional Long Short-Term Memory (Bi-LSTM)

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Tsinghua Science and Technology
Volume 29 Issue 2,
April 2024
Pages 481-491
DOI: 10.26599/TST.2023.9010021
Cite this article:
Peng S, Zeng R, Liu H, et al. Deep Broad Learning for Emotion Classification in Textual Conversations. Tsinghua Science and Technology, 2024, 29(2): 481-491. https://doi.org/10.26599/TST.2023.9010021

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Received: 22 November 2022
Revised: 18 February 2023
Accepted: 19 March 2023
Published: 22 September 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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