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Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Renzhi Gao, Xiaoyu Yao, Zhao Wang, Mohammad Abedin

European Journal of Operational Research, Volume: 314, Issue: 3, Pages: 1159 - 1173

Swansea University Author: Mohammad Abedin

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DOI (Published version): 10.1016/j.ejor.2023.11.035

Abstract

Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ o...

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Published in: European Journal of Operational Research
ISSN: 0377-2217
Published: Elsevier BV 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65091
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Abstract: Time-sync comment (TSC) has emerged as a new type of textual comment for real-time user interactions on online video platforms. The sentiment classification of TSCs provides considerable potential for platforms to optimize operation strategies but inevitably faces great challenges due to the TSCs’ often uninformative and informal text. Considering the contextual dependency among TSCs posted within the same video clip, this study posits that contextual TSCs may benefit the sentiment classification of a target TSC. To address the challenges of leveraging contextual TSCs, such as their semantic representation and fusion, we propose a semi-supervised hierarchical deep learning method for the sentiment classification of TSCs. We design a hierarchical architecture to capture the semantics of TSCs at the word, comment, and context levels. Considering the varying importance of words and comments, we also design attention mechanisms to focus on important sentiment information and fuse semantic representations. Empirical evaluation shows that the proposed method outperforms benchmarked sentiment classification methods. This study advances our knowledge of contextual information indicative of TSC sentiment, and contributes to improving the service operation of online video platforms.
Keywords: OR in marketing; Time-sync comment; Sentiment classification; Contextual dependency; Semi-supervised deep learning
College: Faculty of Humanities and Social Sciences
Funders: This work was supported by the National Natural Science Foundation of China [Grant 72101073], the Natural Science Foundation of Anhui Province [Grant 2108085MG234], and the Fundamental Research Funds for the Central Universities [Grant JZ2023YQTD0075].
Issue: 3
Start Page: 1159
End Page: 1173

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