Year: 2017
Type: conference-paper
Abstract: The paper introduces a deep learningbased Twitter hate-speech text classification system.The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech.Four Convolutional Neural Network models were trained on resp.character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams.The feature set was down-sized in the networks by maxpooling, and a softmax function used to classify tweets.Tested by 10-fold crossvalidation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3% F-score.
DOI: 10.18653/v1/w17-3013
Authors: Björn Gambäck, Utpal Kumar Sikdar
Institution: Norwegian University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2741065173