Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

Author/Creator ORCID




Citation of Original Publication

Singh, Oyesh Mann; Timilsina, Sandesh; Bal, Bal Krishna; Joshi, Anupam; Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts; UMBC HPCF;


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With 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 respectively