Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts

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dc.contributor.authorSingh, Oyesh Mann
dc.contributor.authorTimilsina, Sandesh
dc.contributor.authorBal, Bal Krishna
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2021-01-20T17:30:05Z
dc.date.available2021-01-20T17:30:05Z
dc.description.abstractWith 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 respectivelyen_US
dc.description.sponsorshipWe would like to express sincere thanks to UMBC CARTA lab for providing us NVIDIA Tesla P100 GPU on IBM Minsky server and also many thanks to UMBC High Performance Computing Facility for providing us NVIDIA Tesla V100 GPU to perform our experiments.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/NepSA_ASONAM.pdfen_US
dc.format.extent8 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2gxo3-wls1
dc.identifier.citationSingh, 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.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/20554
dc.language.isoen_USen_US
dc.publisherUMBC HPCFen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis 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.
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleAspect Based Abusive Sentiment Detection in Nepali Social Media Textsen_US
dc.typeTexten_US

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