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Hateminers : Detecting Hate speech against Women

2018-12-17Code Available0· sign in to hype

Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee

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Abstract

With the online proliferation of hate speech, there is an urgent need for systems that can detect such harmful content. In this paper, We present the machine learning models developed for the Automatic Misogyny Identification (AMI) shared task at EVALITA 2018. We generate three types of features: Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet. These features are then concatenated and fed into the machine learning models. Our model came First for the English Subtask A and Fifth for the English Subtask B. We release our winning model for public use and it's available at https://github.com/punyajoy/Hateminers-EVALITA.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
Automatic Misogynistic IdentificationLogistic RegressionAccuracy0.7Unverified

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