2018/12/17 by Punyajoy Saha, Saha, Punyajoy, Binny Mathew +5 · 37 citations
Computer Science · Psychology · Social Sciences · #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Freedom of Expression and Defamation #Hate Speech and Cyberbullying Detection #Linguistics #Philosophy #Political science #Psychology #Social and Information Networks (cs.SI) #Speech recognition #Swearing, Euphemism, Multilingualism #cs.CL #cs.SI
paper · pdf · doi:10.48550/arxiv.1812.06700
published in arXiv (Cornell University) (Cornell University) · 5 Pages, 2 Figures, 1 Table, Model Available at https://github.com/punyajoy/Hateminers-EVALITA
arxiv created 2018/12/17 · openalex publication_date 2018/12/17 · arxiv updated 2018/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.