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About Deep Learning For Hate Speech Detection in Tweets (Pinkesh Badjatiya and Others)
This document presents research on using deep learning techniques for hate speech detection in tweets. The researchers experimented with various classifiers like logistic regression, random forest, SVMs, and gradient boosted decision trees using different tweet embeddings generated by fastText, CNNs, LSTMs, TF-IDF, bag-of-words, and char n-grams. Their best model was a gradient boosted decision tree classifier using embeddings from a CNN trained on tweet text, which achieved an F1 score of 0.864, an improvement of around 18 points over previous work. The embeddings learned by their DNNs captured semantic biases towards words like "pakistan", "female", and "muslims" that were not found using static embeddings.
Detailed Information
| Author: | ['Jahnavi Behara'] |
|---|---|
| Publication Year: | 2023 |
| Pages: | 3 |
| Language: | English |
| Format: | |
| Price: | FREE |
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