Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline

Feng Ge, Weizhao Li, Haopeng Ren, Yi Cai


Abstract
Sentiment analysis in social media is challenging since posts are short of context. As a popular way to express emotion on social media, stickers related to these posts can supplement missing sentiments and help identify sentiments precisely. However, research about stickers has not been investigated further. To this end, we present a Chinese sticker-based multimodal dataset for the sentiment analysis task (CSMSA). Compared with previous real-world photo-based multimodal datasets, the CSMSA dataset focuses on stickers, conveying more vivid and moving emotions. The sticker-based multimodal sentiment analysis task is challenging in three aspects: inherent multimodality of stickers, significant inter-series variations between stickers, and complex multimodal sentiment fusion. We propose SAMSAM to address the above three challenges. Our model introduces a flexible masked self-attention mechanism to allow the dynamic interaction between post texts and stickers. The experimental results indicate that our model performs best compared with other models. More researches need to be devoted to this field. The dataset is publicly available at https://github.com/Logos23333/CSMSA.
Anthology ID:
2022.coling-1.591
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6795–6804
Language:
URL:
https://aclanthology.org/2022.coling-1.591
DOI:
Bibkey:
Cite (ACL):
Feng Ge, Weizhao Li, Haopeng Ren, and Yi Cai. 2022. Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6795–6804, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and Baseline (Ge et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.591.pdf
Data
MOD