Summary of Impact Of Emoji Exclusion on the Performance Of Arabic Sarcasm Detection Models, by Ghalyah H. Aleryani et al.
Impact of emoji exclusion on the performance of Arabic sarcasm detection models
by Ghalyah H. Aleryani, Wael Deabes, Khaled Albishre, Alaa E. Abdel-Hakim
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper addresses the challenge of detecting sarcasm in Arabic speech on social media, highlighting the need for more sophisticated methods to interpret sarcastic expressions. The authors investigate the impact of preprocessing emojis on the performance of sarcasm detection models, adapting AraBERT pre-training models by excluding emojis to improve accuracy. By removing emojis from datasets, the study demonstrates a significant boost in sarcasm detection capabilities, refining language interpretation and eliminating potential confusion introduced by non-textual elements. The results establish new benchmarks in Arabic natural language processing and provide valuable insights for social media platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand what someone means when they’re being sarcastic on social media. It’s harder than you think, especially if the person is speaking in a different language like Arabic! This paper tries to solve this problem by looking at how emojis affect our ability to detect sarcasm. They found that removing emojis from the text actually helps computers understand sarcasm better. This could be really important for social media platforms and how they handle online communication. |
Keywords
» Artificial intelligence » Natural language processing
