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

被引：6
|
作者：
Ding, Keyang [1 ,2 ]
Fan, Chuang [1 ,2 ]
Ding, Yiwen [1 ,2 ]
Wang, Qianlong [1 ,2 ]
Wen, Zhiyuan [1 ,2 ]
Li, Jing [3 ]
Xu, Ruifeng [1 ,2 ,4 ]
机构：
[1] Harbin Inst Technol Shenzhen, Sch Comp Sci & Technol, Shenzhen 518055, Peoples R China
[2] Guangdong Prov Key Lab Novel Secur Intelligence Te, Shenzhen 518055, Peoples R China
[3] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
[4] Peng Cheng Lab, Shenzhen, Peoples R China
来源：
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS | 2024年 / 11卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Attention mechanism; neural topic model; social emotion prediction; social media; MODEL; AGGREGATION;
D O I：
10.1109/TCSS.2023.3334296
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：3362 / 3375
页数：14
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