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Hate speech detection framework shows limited impact from hierarchy and sentiment features

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "HateSense: Hierarchical Sentiment Analysis Framework for Hate Speech Detection" explores the effectiveness of hierarchical classification and sentiment features in identifying hate speech. The study found that while class-weighted training significantly improved hate speech detection F1 scores, hierarchical classification and sentiment features had minimal impact. The research also highlighted that sentiment analysis alone is insufficient for distinguishing between hateful and merely offensive content, and cross-dataset transfer can lead to overstated real-world reliability. AI

IMPACT This research highlights limitations in current hate speech detection methods, suggesting a need for more robust approaches beyond simple hierarchical classification and sentiment analysis.

RANK_REASON Research paper published on arXiv detailing a framework for hate speech detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Hate speech detection framework shows limited impact from hierarchy and sentiment features

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Research paper published on arXiv detailing a framework for hate speech detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Faria Naznin, Md Touhidur Rahman, Shahran Rahman Alve ·

    HateSense: Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy

    arXiv:2411.05819v2 Announce Type: replace-cross Abstract: Hate speech detectors must separate hateful posts from the far more common offensive but non-hateful ones, and two popular ideas for doing so are hierarchical (coarse-to-fine) classification and sentiment features. We revi…

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