ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection

2019-06-01 · SEMEVAL 2019 6 · Arup Baruah, Ferdous Barbhuiya, Kuntal Dey

In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled {''}HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter{''}. This paper presents the results obtained for Subtask A for English language. The results of the BiLSTM and LR models are compared for two different types of preprocessing. One with no stemming performed and no stopwords removed. The other with stemming performed and stopwords removed. The BiLSTM model without attention performed the best for the first test, while the LR model with character n-grams performed the best for the second test. The BiLSTM model obtained an F1 score of 0.51 on the test set and obtained an official ranking of 8/71.

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Hate Speech Detection

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