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Papers/Hateminers : Detecting Hate speech against Women

Hateminers : Detecting Hate speech against Women

Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee

2018-12-17Hate Speech DetectionSentence EmbeddingsBIG-bench Machine Learning
PaperPDFCodeCode(official)

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.

Results

TaskDatasetMetricValueModel
Abuse DetectionAutomatic Misogynistic IdentificationAccuracy0.704Logistic Regression
Hate Speech DetectionAutomatic Misogynistic IdentificationAccuracy0.704Logistic Regression

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