Sajit is an international student from Nepal in his second year at Western Sydney University studying a Bachelor of Computer Science. He has previously completed a research project on astronomy which involved optimising radio telescope scanning patterns. His main areas of interest lie in data science, programming and statistics.
Apart from studying, Sajit enjoys reading, playing chess and photography.
Detection of Australian Racism in Social Networks
Racism is prevalent in social media, but difficult to detect due to its language dependence. It is infeasible to use manual methods to detect racism in social media posts due to the large volumes of data, therefore automated methods are required. Unfortunately, the effectiveness of any automated method is heavily dependent on how we present the social media posts to the classifying machine. In this project, we will be examining the utility of word embedding methods word2vec and GloVe, for representing social media posts, to be used for classifying racism. Our research question is: ‘Do word embeddings trained on problem-specific text provide a more accurate representation than those trained using unspecific text?’. We will be using Google’s word embeddings for the unspecific representations and social media text for the specific representations.