Abstract
Large language models have revolutionized the field of Natural Language Processing. While researchers have assessed their effectiveness for various English language applications, a research gap exists for their application in low-resource regional languages like Marathi. The research presented in this paper intends to fill that void by investigating the feasibility and usefulness of employing large language models for sentiment analysis in Marathi as a case study. The study gathers a diversified and labeled dataset from Twitter that includes Marathi text with opinions classified as positive, negative, or neutral. We test the appropriateness of pre-existing language models such as Multilingual BERT (M-BERT), indicBERT, and GPT-3 ADA on the obtained dataset and evaluate how they performed on the sentiment analysis task. Typical assessment metrics such as accuracy, F1 score, and loss are used to assess the effectiveness of sentiment analysis models. This research paper presents additions to the growing area of sentiment analysis in languages that have not received attention. They open up possibilities for creating sentiment analysis tools and applications specifically tailored for Marathi-speaking communities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agüero-Torales, M.M., Salas, J.I.A., López-Herrera, A.G.: Deep learning and multilingual sentiment analysis on social media data: an overview. Appl. Soft Comput. 107, 107373 (2021)
Ansari, M.A., Govilkar, S.: Sentiment analysis of mixed code for the transliterated Hindi and Marathi texts. Int. J. Nat. Lang. Comput. (IJNLC) 7 (2018)
Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S.: On the dangers of stochastic parrots: can language models be too big?. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 610–623 (2021)
Chathuranga, P., Lorensuhewa, S., Kalyani, M.: Sinhala sentiment analysis using corpus based sentiment lexicon. In: 2019 19th International Conference on Advances in ICT for Emerging Regions (ICTer), vol. 250, pp. 1–7. IEEE (2019)
Deshmukh, R., Kiwelekar, A.W.: Deep convolutional neural network approach for classification of poems. In: Kim, J.-H., Singh, M., Khan, J., Tiwary, U.S., Sur, M., Singh, D. (eds.) IHCI 2021. LNCS, vol. 13184, pp. 74–88. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98404-5_7
Deshmukh, S., Patil, N., Rotiwar, S., Nunes, J.: Sentiment analysis of Marathi language. Int. J. Res. Publ. Eng. Technol. [IJRPET] 3, 93–97 (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dhumal Deshmukh, R., Kiwelekar, A.: Deep learning techniques for part of speech tagging by natural language processing. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 76–81 (2020)
Gillioz, A., Casas, J., Mugellini, E., Abou Khaled, O.: Overview of the transformer-based models for NLP tasks. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 179–183. IEEE (2020)
Han, X., et al.: Pre-trained models: past, present and future. AI Open 2, 225–250 (2021)
Jain, K., Deshpande, A., Shridhar, K., Laumann, F., Dash, A.: Indic-transformers: an analysis of transformer language models for Indian languages. arXiv preprint arXiv:2011.02323 (2020)
Khan, R., Shrivastava, P., Kapoor, A., Tiwari, A., Mittal, A.: Social media analysis with AI: sentiment analysis techniques for the analysis of twitter COVID-19 data. J. Crit. Rev 7(9), 2761–2774 (2020)
Kublik, S., Saboo, S.: GPT-3. O’Reilly Media, Inc. (2022)
Kulkarni, A., Mandhane, M., Likhitkar, M., Kshirsagar, G., Joshi, R.: L3CubeMahaSent: a Marathi tweet-based sentiment analysis dataset. arXiv preprint arXiv:2103.11408 (2021)
Lahoti, P., Mittal, N., Singh, G.: A survey on NLP resources, tools, and techniques for Marathi language processing. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 22(2), 1–34 (2022)
Min, B., et al.: Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput. Surv. (2021)
Naseem, U., Razzak, I., Khan, S.K., Prasad, M.: A comprehensive survey on word representation models: from classical to state-of-the-art word representation language models. Trans. Asian Low-Resour. Lang. Inf. Process. 20(5), 1–35 (2021)
Nozza, D., Bianchi, F., Hovy, D.: What the [mask]? Making sense of language-specific BERT models. arXiv preprint arXiv:2003.02912 (2020)
Patil, R.S., Kolhe, S.R.: Supervised classifiers with TF-IDF features for sentiment analysis of Marathi tweets. Soc. Netw. Anal. Min. 12(1), 51 (2022)
Sawicki, P., et al.: On the power of special-purpose GPT models to create and evaluate new poetry in old styles (2023)
Smith, S., et al.: Using deepspeed and megatron to train megatron-turing NLG 530B, a large-scale generative language model. arXiv preprint arXiv:2201.11990 (2022)
Soong, H.C., Jalil, N.B.A., Ayyasamy, R.K., Akbar, R.: The essential of sentiment analysis and opinion mining in social media: introduction and survey of the recent approaches and techniques. In: 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 272–277. IEEE (2019)
Torfi, A., Shirvani, R.A., Keneshloo, Y., Tavaf, N., Fox, E.A.: Natural language processing advancements by deep learning: a survey. arXiv preprint arXiv:2003.01200 (2020)
Vidyavihar, M.: Sentiment analysis in Marathi language. Int. J. Recent Innov. Trends Comput. Commun. 5(8), 21–25 (2017)
Zhou, C., et al.: A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT. arXiv preprint arXiv:2302.09419 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Data and Model Availability
The dataset and working models for the proposed article are available on the GitHub repository. The link to the GitHub repository is https://github.com/CompDbatu/MarathiSentimentAnalysis.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gaikwad, H., Kiwelekar, A., Laddha, M., Shahare, S. (2024). Adopting Pre-trained Large Language Models for Regional Language Tasks: A Case Study. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14531. Springer, Cham. https://doi.org/10.1007/978-3-031-53827-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-53827-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53826-1
Online ISBN: 978-3-031-53827-8
eBook Packages: Computer ScienceComputer Science (R0)