← 研踪 PaperTrailarXiv:2411.05819 · v3
HierSentNet: Hierarchical Sentiment Analysis Framework for Hate Speech Detection Using Binary and Multiclass Classification
首次提交 2024-11-03 12:11
版本更新 2026-11-03 01:50
雷达观测 2026-11-04 23:41
摘要
Hate speech detectors must separate hateful posts from the far more common offensive ones, and two popular ideas for doing so are hierarchical classification and sentiment features. We define HierSentNet, a framework that combines a dominant-class-first hierarchy with sentiment features, and test each component in a controlled study on Davidson et al. (24,783 tweets) and HateXplain (19,229 posts) with five random splits and paired significance tests. With linear n-gram models, the dominant-class-first hierarchy of HierSentNet lowered macro-F1 by 1.1-1.5 points (p<=0.007), and VADER sentiment features never changed class-weighted Davidson macro-F1 significantly, because hateful and offensive tweets are almost equally negative. Class weighting was the only large effect (+2.0 to +3.5 macro-F1, +5.9 to +9.1 hate F1). These results held for NRC emotion features, soft hierarchical inference, smaller training sets, other imbalance remedies and a convolutional network. On OLID, whose labels are hierarchical, a matching hierarchy improved only its first decision (+1.4 macro-F1, p=0.009), and sentiment helped (+2.8, p=0.005) because there it separates offensive from non-offensive text. Robustness is the main weakness: cross-dataset transfer cut macro-F1 to 43-53, a reclaimed slur doubled the false hate rate, hate recall ranged from 54% to 85% across target communities, and on the HateCheck functional tests the detectors caught only 12-31% of hateful cases. Errors concentrate where annotators disagree, and temperature scaling restores the calibration lost to class weighting.
作者备注：14 pages, 7 figures, 12 tables
观测到的版本
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v3 · 2026-11-03 01:50 · 最新观测版本
HierSentNet: Hierarchical Sentiment Analysis Framework for Hate Speech Detection Using Binary and Multiclass Classification
Hate speech detectors must separate hateful posts from the far more common offensive ones, and two popular ideas for doing so are hierarchical classification and sentiment features. We define HierSentNet, a framework that combines a dominant-class-first hierarchy with sentiment features, and test each component in a controlled study on Davidson et al. (24,783 tweets) and HateXplain (19,229 posts) with five random splits and paired significance tests. With linear n-gram models, the dominant-class-first hierarchy of HierSentNet lowered macro-F1 by 1.1-1.5 points (p<=0.007), and VADER sentiment features never changed class-weighted Davidson macro-F1 significantly, because hateful and offensive tweets are almost equally negative. Class weighting was the only large effect (+2.0 to +3.5 macro-F1, +5.9 to +9.1 hate F1). These results held for NRC emotion features, soft hierarchical inference, smaller training sets, other imbalance remedies and a convolutional network. On OLID, whose labels are hierarchical, a matching hierarchy improved only its first decision (+1.4 macro-F1, p=0.009), and sentiment helped (+2.8, p=0.005) because there it separates offensive from non-offensive text. Robustness is the main weakness: cross-dataset transfer cut macro-F1 to 43-53, a reclaimed slur doubled the false hate rate, hate recall ranged from 54% to 85% across target communities, and on the HateCheck functional tests the detectors caught only 12-31% of hateful cases. Errors concentrate where annotators disagree, and temperature scaling restores the calibration lost to class weighting.
查看此版本解读 →查看此版本原文 ↗v2 · 2026-10-27 22:21
HateSense: Hierarchical Sentiment Analysis Framework for Hate Speech Detection: Implementing Binary and Multiclass Classification Strategy
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 revisit our earlier transformer-based framework, which combines both ideas, and test each component in a controlled study on two public benchmarks (Davidson et al., 24,783 tweets; HateXplain, 19,229 posts) with five random splits, validation-based tuning, and paired bootstrap tests. With linear models over word and character n-grams, the dominant-class-first hierarchy used in our framework lowered macro-F1 by 1.1-1.5 points relative to a flat classifier (p <= 0.007), and adding VADER sentiment features changed Davidson macro-F1 by at most 0.6 points, never significantly in the class-weighted settings. Class-weighted training was the only intervention with a large effect (+2.0 to +3.5 macro-F1 and +5.9 to +9.1 hate-class F1), and sentiment cannot separate the two abusive classes because hateful and offensive tweets are almost equally negative (mean VADER compound -0.35 vs. -0.29). Cross-dataset transfer cut macro-F1 to 43-53, and a reclaimed slur doubled the false hate rate of a logistic-regression detector, showing that in-domain scores overstate real-world reliability.
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