Application of Nepali Large Language Models to Improve Sentiment Analysis
Author:
Affiliation:
1. Institute of Engineering, Nepal
2. Fusemachines Pvt. Ltd, Nepal
3. Advanced College of Engineering and Management, Nepal
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3647782.3647804
Reference20 articles.
1. Sitaula C. (2021) Deep learning-based methods for sentiment analysis on Nepali covid-19 related tweets Computational Intelligence and Neuroscience. Available at: https://www.hindawi.com/journals/cin/2021/2158184/ (Accessed: 23 November 2023).
2. Prottasha, N.J. (2022) Transfer learning for sentiment analysis using Bert-based supervised fine-tuning, MDPI. Available at: https://www.mdpi.com/1424-8220/22/11/4157 (Accessed: 23 November 2023).
3. Chaklam Silpasuwanchai Suyogya Ratna Tamrakar 2021 Comparative evaluation of transformer-based Nepali language models. ResearchSquare.
4. Building machine learning-based senti-word lexicon for sentiment analysis. Available at: https://www.researchgate.net/publication/273917156_Building_Machine_Learning_Based_Senti-word_Lexicon_for_Sentiment_Analysis (Accessed: 16 September 2023).
5. Xiaoyi Zhao and Yukio Ohsawa "Sentiment Analysis on the Online Reviews Based on Hidden Markov Model " Vol. 9 No. 2 pp. 33-38 May 2018. doi: 10.12720/jait.9.2.33-38
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