SENTIMENT ANALYSIS RESEARCH AND IMPORTANCE

Authors

  • Suyunova Malika Adil kizi Tashkent State University named after Alisher Navoi University of Uzbek Language and Literature Computational linguistics and digital technologies PhD student of the department

Keywords:

sentiment analysis, language processing, emotional analysis, natural language processing (NLP), cross-lingual research, semantics, linguistics, social networks, artificial intelligence, grammatical analysis.

Abstract

This article examines the issues of sentiment analysis, which is a branch of computational linguistics, and its study in the context of world linguistics. Computational linguistics is a scientific field focused on understanding and processing human language with the help of computers, and the importance of sentiment analysis is growing day by day. By detecting and classifying emotional states in text, it is possible to analyze various practical areas, including social networks, customer reviews, and other texts. The article provides detailed information on the main methods and techniques of sentiment analysis, as well as approaches to its study in linguistics. It emphasizes the importance of the semantic characteristics of language, contextual analysis, and cross-lingual research.

Downloads

Download data is not yet available.

References

Ismoilov, R. (2020). Computational Linguistics: Theoretical Foundations and Practical Applications. Academy of Sciences of the Republic of Uzbekistan.

Sattorov, F. (2018). Methodology of text processing and sentiment analysis. Linguistics and Computational Linguistics, 12(3), 45-59.

Azimov, M., & Akhmedov, S. (2021). Linguistics and sentiment analysis: New approaches and research. In Uzbek Linguistics Journal 15(2), 118-134.

Allen, J. (1995). Natural Language Understanding. Addison-Wesley.

Jurafsky, D., & Martin, JH (2020). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson.

G.S.Matlatipov. SENTIMENTAL ANALYSIS OF THE UZBEKISTAN LANGUAGE. Abstract. 6 p.

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).b

Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts (2013). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank.

Zadeh, A., Chen, M., Liang, J., Poria, S., & Morency, LP (2016). CMU-MOSI: A Multimodal Sentiment Analysis Dataset. Proceedings of the 2016 IEEE International Conference on Data Mining (ICDM) , 2016, 1-10. https://doi.org/10.1109/ICDM.2016.0042.

Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT.

Downloads

Published

2025-03-31

How to Cite

Suyunova Malika Adil kizi. (2025). SENTIMENT ANALYSIS RESEARCH AND IMPORTANCE. Journal of Applied Science and Social Science, 15(03), 891–897. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/881

Issue

Vol. 15 No. 03 (2025): Volume 15 Issue 03

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All content published in the Journal of Applied Science and Social Science (JASSS) is protected by copyright. Authors retain the copyright to their work, and grant JASSS the right to publish the work under a Creative Commons Attribution License (CC BY). This license allows others to distribute, remix, adapt, and build upon the work, even commercially, as long as they credit the author(s) for the original creation.