Singh, Oyesh MannTimilsina, SandeshBal, Bal KrishnaJoshi, Anupam2021-01-202021-01-20Singh, Oyesh Mann; Timilsina, Sandesh; Bal, Bal Krishna; Joshi, Anupam; Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts; UMBC HPCF; http://hpcf-files.umbc.edu/research/papers/NepSA_ASONAM.pdfhttp://hdl.handle.net/11603/20554With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked datasets for high-resource languages (English, French, and German) in specific domains like restaurants, hotels or electronic goods but not in low-resource languages like Nepali. In this paper, we present our work to create a dataset for the targeted aspect-based sentiment analysis in the social media domain, set up a dataset benchmark and evaluate using various machine learning models. The dataset comprises of code-mixed and code-switched comments extracted from Nepali YouTube videos. We present convincing baselines using a multilingual BERT model for the Aspect Term Extraction task and BiLSTM model for the Sentiment Classification Task achieving 57.978% and 81.60% F1 score respectively8 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.UMBC High Performance Computing Facility (HPCF)Text