  Online social platforms have been the battlefield of users with different
emotions and attitudes toward each other in recent years. While sexism has been
considered as a category of hateful speech in the literature, there is no
comprehensive definition and category of sexism attracting natural language
processing techniques. Categorizing sexism as either benevolent or hostile
sexism is so broad that it easily ignores the other categories of sexism on
social media. Sharifirad S and Matwin S 2018 proposed a well-defined category
of sexism including indirect harassment, information threat, sexual harassment
and physical harassment, inspired from social science for the purpose of
natural language processing techniques. In this article, we take advantage of a
newly released dataset in SemEval-2018 task1: Affect in tweets, to show the
type of emotion and intensity of emotion in each category. We train, test and
evaluate different classification methods on the SemEval- 2018 dataset and
choose the classifier with highest accuracy for testing on each category of
sexist tweets to know the mental state and the affectual state of the user who
tweets in each category. It is a nice avenue to explore because not all the
tweets are directly sexist and they carry different emotions from the users.
This is the first work experimenting on affect detection this in depth on
sexist tweets. Based on our best knowledge they are all new contributions to
the field; we are the first to demonstrate the power of such in-depth sentiment
analysis on the sexist tweets.
