Open AccessHate Speech Detection in Portuguese with Naïve Bayes, SVM, MLP and Logistic Regression
Author(s) -
Adriano Silva,
Norton Roman
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12112
Subject(s) - support vector machine , computer science , benchmark (surveying) , artificial intelligence , naive bayes classifier , machine learning , portuguese , logistic regression , set (abstract data type) , task (project management) , bayes' theorem , speech recognition , pattern recognition (psychology) , bayesian probability , engineering , linguistics , philosophy , geodesy , systems engineering , programming language , geography
Even though social networks can provide free space for discussing ideas, people can also use them to propagate hate speech and, given the amount of written material in such networks, it becomes necessary to rely on automatic methods for identifying this problem. In this work, we set out to verify the use of some classic Machine Learning algorithms for the task of hate speech detection in tweets written in Portuguese, by testing four different models (SVM, MLP, Logistic Regression and Naïve Bayes) with different configurations. Results show that these algorithms produce better results (in terms of micro-averaged F1 score) than the LSTM used for benchmark, being also competitive to other results by the related literature