Understanding Language and Culture Through Sentiment Analysis: A Conceptual Framework

Authors

  • Sheema Liza Idris Academy of Language Studies, UiTM Perak Branch, Seri Iskandar Kampus, 32610 Seri Iskandar, Perak, Malaysia
  • Masurah Mohamad Faculty of Computer and Mathematical Sciences, UiTM Perak Branch, Tapah Campus, Tapah Road, 35400 Tapah Road, Perak

DOI:

https://doi.org/10.37134/kupasseni.vol14.sp.11.2026

Keywords:

Language and Culture, Sentiment Analysis, Conceptual Framework, Cross-Cultural Communication, Linguistic Approaches

Abstract

This article presents a conceptual framework that facilitates understanding of how language and culture influence emotional expressions in the online world. Although sentiment analysis is a popular instrument for data analysis, the majority of computational studies focus on the algorithm's accuracy and thus ignore the cultural and linguistic aspects of the data. This paper merges the linguistic, cultural, and computational elements to demonstrate that language, culture, and emotions are interconnected and form a triangle. The proposed framework consists of three layers: the Language Layer (lexical and discourse features), the Culture Layer (norms, values, and communication styles), and the Sentiment Layer (polarity, emotional tone, and intensity). These stages are always interacting with each other, culture shapes language, and both language and culture determine the way emotions are expressed and recognised. By integrating these different aspects, the research constitutes a richer, interdisciplinary approach to sentiment analysis in different cultural contexts. The presented framework is an interdisciplinary one and may serve as a guide in the fields of linguistics, intercultural communication, education, and digital marketing, where the comprehension of the emotional expressions of different cultures is of utmost significance.

Downloads

Download data is not yet available.

References

Alharbi, A., & Alshahrani, M. (2025). Generalizing sentiment analysis: A review of progress, challenges, and emerging directions. Social Network Analysis and Mining, 15(1). https://doi.org/10.1007/s13278-025-01461-8

Amos, Z. (2024, July 26). How cultural differences impact sentiment analysis. Datafloq. https://datafloq.com/how-cultural-differences-impact-sentiment-analysis/

Anggia, P., & Sumi, K. (2024). Enhancing persuasion through emotional elicitation in digital marketing: A cross-cultural comparative study. Proceedings of the 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), 2101–2106. https://ieeexplore.ieee.org/document/10633475

Barik, K., Misra, S. (2024) Analysis of customer reviews with an improved VADER lexicon classifier. J Big Data 11(10) . https://doi.org/10.1186/s40537-023-00861-x

Barnes, J., Øvrelid, L., & Velldal, E. (2019). Sentiment analysis is not solved! Assessing and probing sentiment classification. arXiv. https://arxiv.org/abs/1906.05887

Bashiri, H., & Naderi, H. (2024). Comprehensive review and comparative analysis of transformer models in sentiment analysis. Knowledge and Information Systems, 66, 7305–7361. https://doi.org/10.1007/s10115-024-02214-3

Brown, P., & Levinson, S. C. (1987). Politeness: Some universals in language usage. Cambridge University Press.

Cambria, E., Liu, Q., Decherchi, S., Xing, F., & Kwok, K. (2022). SenticNet 7: A commonsense-based neurosymbolic AI framework for explainable sentiment analysis. In Proceedings of the 13th Language Resources and Evaluation Conference (pp. 3829–3839). European Language Resources Association. https://aclanthology.org/2022.lrec-1.408/

Cela, H., Veit, SV. & Wood, G. (2024) Breaking down the laughter: an exploration into the linguistic dimensions in stand-up comedy ratings. BMC Psychol 12, 679. https://doi.org/10.1186/s40359-024-02187-6.

Chen, K., Wang, S., Ben, H., Tang, S., & Hao, Y. (2025). Mixture of multimodal adapters for sentiment analysis. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 1822–1833). Association for Computational Linguistics. https://aclanthology.org/2025.naacl-long.90/

Dubey, P., Dubey, P., & Bokoro, P. N. (2025). Unpacking Sarcasm: A Contextual and Transformer-Based Approach for Improved Detection. Computers, 14(3), 95. MDPI. https://www.mdpi.com/2073-431X/14/3/95

Effendi, P. (2021). Crazy Polite Asians: Politeness Strategy and Cultural Relativism in Asian-Western Environment. Language Horizon. Journal of Language Studies, 9(1). https://doi.org/10.5281/zenodo.5646916

Hall, E. T. (1976). Beyond culture. Anchor Books.

Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (3rd ed.). McGraw-Hill.

Huang, M. (2024). Cross-cultural communication in the digital era: Insights from social media interactions. LNEP, 54. https://www.ewadirect.com/proceedings/LNEP/article/view/13156

Jiang, B., Liu, L., Guo, Z., & Wang, Y. (2022). A study of intercultural communicative competence in ELT. Advances in Journalism and Communication, 10(3), 275–289. https://doi.org/10.4236/ajc.2022.103018

Lei, L., & Liu, D. (2022). Conducting sentiment analysis. Cambridge University Press. https://www.cambridge.org/core/elements/conducting-sentiment-analysis/B00BACADE638BF1AD5F61972FEE4183D

Li, E., Li, T., Liang, T., Kang, A., Chen, K., & Luo, H. (2025). Cross-lingual sentiment analysis empowered by emotional mutual reinforcement through emojis. International Journal of Machine Learning and Cybernetics, 16, 6031–6045. https://doi.org/10.1007/s13042-025-02610-3

Li, Y., Zhang, H., & Zhou, M. (2023). Exploring cultural meaning construction in social media: An analysis of online discourse. Journal of Intercultural Communication, 23(4), 45–62. https://immi.se/index.php/intercultural/article/download/Li-et-al-2023-4/637

Li, Y., Zhang, H., & Zhou, M. (2025). Cultural bias matters: A cross-cultural benchmark dataset and sentiment analysis framework. Proceedings of the 2025 ACL Conference. https://aclanthology.org/2025.acl-long.1275.pdf

Lou, C., & Chen, J. (2025). What is brand activism? Explicating consumers’ perceptions of its characteristics, authenticity, and effectiveness. ResearchGate PDF

Martin, J. R., & White, P. R. R. (2005). The language of evaluation: Appraisal in English. Palgrave Macmillan. https://link.springer.com/book/10.1057/9780230511910

Mohamed, K. M. (2025). Visual storytelling and cultural connection in GCC social media advertising. Frontiers in Communication, 10. https://doi.org/10.3389/fcomm.2025.1584156

Mohammed, A. M. K., Nawaz Ali, G. G. M., & Khairunnesa, S. S. (2025). GSAF: An ML-Based Sentiment Analytics Framework for Understanding Contemporary Public Sentiment and Trends on Key Societal Issues. Information, 16(4), 271. MDPI. https://doi.org/10.3390/info16040271

Rajik, J. (2025). Politeness Strategies in Political Discourse: A Study through the Lens of Brown and Levinson's Politeness Theory. Journal of Pragmatics and Discourse Analysis, 4(1), 26-33 https://doi.org/10.32996/jpda.2025.4.1.4.

Semary, N. A., Ahmed, W., Amin, K., Pławiak, P., & Hammad, M. (2023). Improving sentiment classification using a RoBERTa-based hybrid model. Frontiers in Human Neuroscience, 17. https://doi.org/10.3389/fnhum.2023.1292010

Yang, S., Zhang, D., Ren, J., Xu, Z., Zhang, X., Song, Y., Lin, H., & Xia, F. (2025). Cultural bias matters: A cross-cultural benchmark dataset and sentiment-enriched model for understanding multimodal metaphors. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://aclanthology.org/2025.acl-long.1275.pdf

Zeng, J., Zhou, J., & Liu, T. (2022). Mitigating inconsistencies in multimodal sentiment analysis under uncertain missing modalities. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2767–2780. https://aclanthology.org/2022.emnlp-main.189/

Downloads

Published

2026-04-27

How to Cite

Idris, S. L., & Mohamad, M. (2026). Understanding Language and Culture Through Sentiment Analysis: A Conceptual Framework. KUPAS SENI: Jurnal Seni Dan Pendidikan Seni, 14(Isu Khas), 129-138. https://doi.org/10.37134/kupasseni.vol14.sp.11.2026