Sentiment analysis based- Hate speech detection of Amharic social media post and comments

Abstract

Social media allows the user to post, comment and communicate freely. And this led to an increasing amount of online hate speech. Online hate speech has different offline repercussions, according to studies. In recent years, hate speech have led to internal violence, relocation, and human rights violations against specific social groups around the world. And Ethiopian societies are among the victims. To lessen the spread of hate speech, this study develops Amharic hate speech detection. The study's main goal is to create a model for detecting hate speech by taking into account sentiment analysis of the relevant datasets and proving a link between hate speech and sentiment analysis. Peacemakers can take action when hate speech comments are being circulated online by using an Amhariclanguage hate speech detection system. Additionally, it will assist owners of social media platforms by automatically reporting hate speech remarks before they are seen by a wider audience Comments were gathered from Facebook, TikTok, and YouTube channels in order to create a labeled large Amharic dataset. Following data cleaning, 79991 hate and hate-free annotated datasets along with their sentiment were obtained. To label the dataset as hate and hate-free, new annotation guidelines were created. Despite previous related work, recent and large dataset were collected and their sentiment were also considered. To construct the model, CNN and GRU deep learnings were used in conjunction with Word embedding features. Negative sentiment was revealed to be the source of hate speech content. And most of the hate free dataset were found to be having positive sentiment. Using datasets that have been annotated by humans as a hate and hate free, the GRU and CNN models demonstrated respective accuracies of 0.90 and 0.72. And when both hate and non-hate annotated datasets along with their sentiment were used in the hate speech detection model, the models' respective accuracies become 0.75 and 0.74. As a result, in both model GRU outperform CNN model, and the CNN approach shows good performance for the hate speech detection model that was developed by integrating sentiment analysis.