Summary of Unleashing the Power Of Emojis in Texts Via Self-supervised Graph Pre-training, by Zhou Zhang et al.
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training
by Zhou Zhang, Dongzeng Tan, Jiaan Wang, Yilong Chen, Jiarong Xu
First submitted to arxiv on: 22 Sep 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 Our paper introduces a novel data mining framework that leverages the power of emojis in social media posts. Unlike existing approaches, which treat emojis as ordinary characters, we construct a heterogeneous graph to represent the interactions between posts, words, and emojis. This allows us to capture the rich semantic information in emojis and their relationships with text. We propose a graph pre-training framework for text and emoji co-modeling, comprising node-level graph contrastive learning and edge-level link reconstruction learning. Our approach outperforms strong baseline methods on two downstream tasks using the Xiaohongshu and Twitter datasets. This research has significant implications for social media data mining, enabling more effective analysis of online content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re scrolling through social media and seeing lots of emojis . Existing ways to analyze these posts don’t take into account how important these little pictures are. They just treat them like normal characters. But what if we could unlock the secrets of these emojis? We created a new way to understand how posts, words, and emojis interact with each other. This lets us capture the meaning behind the emojis and how they relate to the text. Our method works really well on two big datasets from Xiaohongshu and Twitter. This research is important because it helps us better analyze online content and what people are saying. |
