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Online social platforms have been the battlefield of users with differentemotions and attitudes toward each other in recent years. While sexism has beenconsidered as a category of hateful speech in the literature, there is nocomprehensive definition and category of sexism attracting natural languageprocessing techniques. Categorizing sexism as either benevolent or hostilesexism is so broad that it easily ignores the other categories of sexism onsocial media. Sharifirad S and Matwin S 2018 proposed a well-defined categoryof sexism including indirect harassment, information threat, sexual harassmentand physical harassment, inspired from social science for the purpose ofnatural language processing techniques. In this article, we take advantage of anewly released dataset in SemEval-2018 task1: Affect in tweets, to show thetype of emotion and intensity of emotion in each category. We train, test andevaluate different classification methods on the SemEval- 2018 dataset andchoose the classifier with highest accuracy for testing on each category ofsexist tweets to know the mental state and the affectual state of the user whotweets in each category. It is a nice avenue to explore because not all thetweets are directly sexist and they carry different emotions from the users.This is the first work experimenting on affect detection this in depth onsexist tweets. Based on our best knowledge they are all new contributions tothe field; we are the first to demonstrate the power of such in-depth sentimentanalysis on the sexist tweets.

How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques