LCSEP: A Large-Scale Chinese Dataset for Social Emotion Prediction to Online Trending Topics

IF 4.5 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS IEEE Transactions on Computational Social Systems Pub Date : 2024-01-02 DOI:10.1109/TCSS.2023.3334296

Keyang Ding;Chuang Fan;Yiwen Ding;Qianlong Wang;Zhiyuan Wen;Jing Li;Ruifeng Xu

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Abstract

In this article, we present our work in social emotion prediction to online trending topics. While most prior works focus on emotion from writers or the readers’ emotions evoked by news articles, we investigate discussions from massive social media users and explore the public feelings to the online trending topic. We employ user-generated “#hashtags” to indicate online trending topics and construct a large-scale Chinese dataset for social emotion prediction (LCSEP) to trending topics collected from the Chinese microblog Sina Weibo. It contains more than 20 000 trending topics, each with social emotions voted in 24 fine-grained types, and gathers hashtags, posts, comments, and related metadata to give each trending topic a thorough context. We also propose a Hashtag- and Topic-Enhanced Attention Model ( HTEAM ) that combines a pretrained BERT model, a neural topic model, and an attention mechanism via joint training to understand social emotion. Experiments show that HTEAM outperforms baselines and achieves the state-of-the-art result.

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LCSEP：用于网络热门话题社交情感预测的大规模中文数据集

在本文中，我们介绍了针对网络流行话题的社会情感预测工作。以往的研究大多集中于作者的情绪或新闻文章引发的读者情绪，而我们则调查来自海量社交媒体用户的讨论，探索公众对网络热门话题的感受。我们使用用户生成的 "#hashtags "来表示网络流行话题，并针对从中国微博新浪微博收集的流行话题构建了一个大规模的中国社会情感预测数据集（LCSEP）。该数据集包含 20,000 多个热门话题，每个热门话题都有 24 种细粒度的社会情感投票，并收集了标签、帖子、评论和相关元数据，为每个热门话题提供了全面的语境。我们还提出了一种标签和话题增强注意力模型（HTEAM），通过联合训练将预训练 BERT 模型、神经话题模型和注意力机制结合起来，从而理解社会情绪。实验表明，HTEAM 的表现优于基线，达到了最先进的水平。

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来源期刊

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.

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