Textual Features extracted for Hate Speech Detection using NLP
Features from the data used for a classification model always have a greater impact on how the model works. Supervised learning is used in hate speech classification models. The various features extracted from the text are
- Simple Surface Features
- Word Generalization
- Sentiment Analysis
- Lexical Resources
- Linguistic Features
- Knowledge-Based Features
- Meta-Information
- Multimodal Information
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Data Science
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Simple Surface Features Needed for Text Classification Tasks
Word Generalization for Hate Speech Classification
Sentiment Analysis Feature Generation for Hate Speech Classification
Lexical Resource Feature for Hate Speech Detection
Linguistic Features For Hate Speech Detection
Knowledge-Based Features for Hate Speech Detection
Meta-Information Feature Extraction for Hate Speech Detection
Multimodal Information Feature Extraction for Hate Speech Detection