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.
| Original language | English |
|---|---|
| Pages (from-to) | 3362-3375 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Computational Social Systems |
| Volume | 11 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 1 Jun 2024 |
Keywords
- Attention mechanism
- neural topic model
- social emotion prediction
- social media
ASJC Scopus subject areas
- Modelling and Simulation
- Social Sciences (miscellaneous)
- Human-Computer Interaction