调整文本预处理顺序并结合集成方法，显著提升社交媒体仇恨内容识别准确率。
Hate Content Detection via Novel Pre-Processing Sequencing and Ensemble Methods
- 通过实验对比不同预处理步骤的先后顺序，找出最优流程。
- 在三个公开数据集上达到最高95.14%的分类准确率。
- 适合关注文本分类优化与网络内容安全的研究者和工程师。
社交媒体，尤其是推特（Twitter），近年来出现了大量如网络欺凌和仇恨言论等事件。因此，识别仇恨言论已成为当务之急。本文提出一种计算框架，用于遏制网络上的仇恨内容。具体而言，该研究系统地探讨了文本预处理操作顺序对仇恨内容识别的影响。最佳预处理序列与支持向量机（SVM）、随机森林（Random Forest）、决策树（Decision Tree）、逻辑回归（Logistic Regression）和K近邻（K-Neighbor）等主流分类器结合后，显著提升了性能。此外，该最优预处理序列还与袋装法（bagging）、提升法（boosting）和堆叠法（stacking）等集成学习方法联合使用，进一步优化结果。采用三个公开基准数据集（WZ-LS、DT、FOUNTA）进行评估，所提方法在测试中达到最高95.14%的准确率，验证了独特预处理流程与集成分类器结合的有效性。
原文摘要 · Abstract (English)
Social media, particularly Twitter, has seen a significant increase in incidents like trolling and hate speech. Thus, identifying hate speech is the need of the hour. This paper introduces a computational framework to curb the hate content on the web. Specifically, this study presents an exhaustive study of pre-processing approaches by studying the impact of changing the sequence of text pre-processing operations for the identification of hate content. The best-performing pre-processing sequence, when implemented with popular classification approaches like Support Vector Machine, Random Forest, Decision Tree, Logistic Regression and K-Neighbor provides a considerable boost in performance. Additionally, the best pre-processing sequence is used in conjunction with different ensemble methods, such as bagging, boosting and stacking to improve the performance further. Three publicly available benchmark datasets (WZ-LS, DT, and FOUNTA), were used to evaluate the proposed approach for hate speech identification. The proposed approach achieves a maximum accuracy of 95.14% highlighting the effectiveness of the unique pre-processing approach along with an ensemble classifier.
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