Hate Sentiment Recognition System For Persian Language

People’s lives in societies are tied to social networks and these networks face problems such as the existence of hateful speech. Most social networks try to identify and prevent the spread of this phenomenon by using natural language processing (NLP) methods. On the internet, hate speech causes arguments between different groups in society. Given that anyone is able to put any content on social media in the form of a short text, this leads to the uncontrollable spread of hatred on social networks and can cause harm to individuals and various groups in society. This is necessary to have control over users’ content on social media. In this study, a method for identifying hateful content in short texts is proposed. First, cosine similarities of word-based and character-based n-grams (features) and sentences (samples) are calculated. Then, employing calibrated support vector machine, the probability of each feature related to hatred is calculated. Finally, another SVM is applied for the final classification. The proposed method is compared to the state-of-the-art methods such as pars-bert and a multi-view SVM approach using Instagram comments in terms of various performance metrics. Results show that the proposed method outperforms the previous studies.