Summary of Enhancing Multimodal Affective Analysis with Learned Live Comment Features, by Zhaoyuan Deng et al.
Enhancing Multimodal Affective Analysis with Learned Live Comment Features
by Zhaoyuan Deng, Amith Ananthram, Kathleen McKeown
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to analyzing emotions in video content is proposed in this paper. The authors develop a dataset called LCAffect, containing user-generated live comments for English and Chinese videos across various genres. These live comments are synchronized with the video and provide insight into viewer reactions and emotions. To leverage these comments for affective analysis, the authors use contrastive learning to train a video encoder that generates synthetic features for enhancing multimodal content analysis. The proposed method is evaluated on sentiment, emotion recognition, and sarcasm detection tasks in both English and Chinese, showing significant improvements over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to better understand people’s emotions while watching videos. It creates a special dataset with comments from viewers that match the video they’re watching. These comments give clues about what people are feeling as they watch different types of content, like movies or music videos. The authors develop a new way to analyze these comments and use them to improve how we understand emotions in video content. They test their approach on various tasks and show it works better than existing methods. |
Keywords
» Artificial intelligence » Encoder
