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About Final Year
1) The documents discuss hate speech detection using machine learning and natural language processing techniques. Several papers are reviewed that evaluate classifiers like SVM, Naive Bayes, CNNs and evaluate their performance on various hate speech datasets. 2) The papers find that techniques like SVM and Random Forest perform best in classifying hate speech and deep learning models like BERT show promising performance. However, limitations around data availability and lack of comparative studies are identified. 3) Advances in machine learning algorithms for hate speech detection are explored with core components like data collection, feature extraction, and model evaluation discussed. Random Forest is found to be the most accurate while issues around data availability and lack remain challenges.
Detailed Information
| Author: | ['Harry Roy'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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