Summary of Impact Of Stickers on Multimodal Chat Sentiment Analysis and Intent Recognition: a New Task, Dataset and Baseline, by Yuanchen Shi et al.
Impact of Stickers on Multimodal Chat Sentiment Analysis and Intent Recognition: A New Task, Dataset and Baseline
by Yuanchen Shi, Biao Ma, Fang Kong
First submitted to arxiv on: 14 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MSAIRS (Multimodal chat Sentiment Analysis and Intent Recognition involving Stickers) task aims to investigate the impact of stickers on sentiment analysis and intent recognition in social media conversations. To achieve this, a novel multimodal dataset is introduced, containing Chinese chat records and stickers from mainstream platforms. The dataset features paired text-sticker combinations with varying sticker images and texts, allowing for better understanding of sticker effects. A joint model, MMSAIR, is proposed to tackle the MSAIRS task, which incorporates visual information from stickers and demonstrates improved performance on the provided datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Stickers are a popular way to express feelings and intentions in social media chats. But have you ever thought about how these little images affect what we say online? A team of researchers is trying to figure out how stickers change the way people communicate. They created a special dataset with lots of chat conversations and corresponding stickers from different social media platforms. By studying this data, they hope to understand how stickers make our online interactions more or less positive (or negative). The goal is to develop better tools for analyzing what we mean when we write something online. |
