This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classification where the system predicts whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguistic features to improve the classifier's performance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency-Inverse Document Frequency (TF-IDF) representation.
The system performance reaches about 81% F1 when it is applied to the training dataset, but its F1 drops to 36% on the official test dataset for the English and 64% for the Spanish language concerning the hate speech class.
Keywords: Hate speech, Linguistic features, Lexicon