Summary of Hierarchical Sentiment Analysis Framework For Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy, by Faria Naznin et al.
Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy
by Faria Naznin, Md Touhidur Rahman, Shahran Rahman Alve
First submitted to arxiv on: 3 Nov 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 This paper proposes a new approach to detecting hate speech on social media, addressing the challenge of distinguishing hate speech from regular offensive language. The authors leverage deep learning and machine learning techniques to develop a multitask model that integrates shared emotional representations. Specifically, they utilize a Transformer-based model from Hugging Face and sentiment analysis to prevent false positives. The proposed system outperforms previous approaches, which have achieved low precision due to their failure to carefully treat related tasks like sentiment analysis and emotion classification. The authors demonstrate the effectiveness of their approach across multiple datasets, showcasing its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech on social media is a big problem that needs to be solved. Right now, computers are bad at figuring out what’s hate speech and what’s just mean language. To get better, researchers are working on new ways to detect hate speech. This paper introduces a new way to do this using special computer models. These models can learn from lots of data and understand the emotions behind the words. The authors test their approach on different datasets and show that it works much better than previous methods. They hope that their work will help make social media safer and more respectful for everyone. |
Keywords
» Artificial intelligence » Classification » Deep learning » Machine learning » Precision » Transformer
