Sentiment Analysis of Sexual Harassment in Malaysia on Twitter Using Machine Learning Algorithms - Nurellezia, Suleiman (Universiti Malaysia Sarawak, (UNIMAS), 2023)
Twitter is one of the most compelling platforms in social media that fosters meaningful interactions and connections between its users from all walks of life. Since it is a platform that supports freedom of speech, a lot of opinions can be analyzed such as sentiment analysis of sexual harassment. While various studies have explored sentiment analysis of sexual harassment, none of it is locally analyzed based on Malaysia region only. Hence, in this report, tweets regarding sexual harassment in Malaysia are extracted using a list of keywords by semiautonomous data annotation to get the correct labelling of the data. The labelling of the data is designed such that positive label, is comprehended as tweets that expressed support towards victims, meanwhile negative sentiments are regarded as tweets that sexually harass other Twitter users or neutral sentiments, such as unbiased opinions and statements. The data then underwent stages of data preparation such as data preprocessing and feature extraction to facilitate further data transformation. The transformed data is then modelled using machine