SENTIMENT ANALYSIS RESEARCH AND IMPORTANCE

Аннотация

This study aims to study various methods in the field of computational linguistics and sentiment analysis and compare their effectiveness. The study extensively analyzed methods such as word-based analysis, machine learning, deep learning, and cross-language analysis. The advantages and disadvantages of each method were identified and their effectiveness in different situations was shown. Word-based analysis methods, although they work quickly and easily, do not take into account the context of the text, and therefore can sometimes lead to errors. Machine learning methods provide accurate results based on more data, but they require a large amount of training data for their effective operation. Deep learning methods, in particular Transformer http://www.internationaljournal.co.in/index.php/jasass 895 Volume15Issue03, March2025 Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023: 6.995, 2024 7.75 networks, are very effective in analyzing the exact and complete context of texts, providing high accuracy, but these methods require a lot of computational resources. Cross-language sentiment analysis is effective in working with multiple languages and allows us to take into account different cultures, but it may face some difficulties in taking into account differences between languages. The results of the study show that the effectiveness of sentiment analysis depends on the chosen method and the characteristics of the text being analyzed. Therefore, using a combination of different methods helps to increase the accuracy of sentiment analysis. In future research, it is possible to create more effective sentiment analysis systems by integrating methods and developing new approaches, in particular, by jointly applying cross-language analysis and deep learning technologies. Listof usedliterature 1. Ismoilov, R. (2020). Computational Linguistics: Theoretical Foundations and Practical Applications. Academy of Sciences oftheRepublicofUzbekistan. 2. Sattorov, F. (2018). Methodology of text processing and sentiment analysis. Linguistics and Computational Linguistics, 12(3), 45-59. 3. Azimov, M., & Akhmedov, S. (2021). Linguistics and sentiment analysis: New approaches and research. In Uzbek Linguistics Journal 15(2), 118-134. 4. Allen, J. (1995). Natural Language Understanding. Addison-Wesley. 5. Jurafsky, D., & Martin, JH(2020). Speech and Language Processing: An Introduction toNatural Language Processing, Computational Linguistics, and Speech Recognition. Pearson. 6. G.S.Matlatipov. SENTIMENTAL ANALYSIS OF THE UZBEKISTAN LANGUAGE. Abstract. 6p. 7. 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 theAssociation for Computational Linguistics(ACL 2011).b 8. Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng and Christopher Potts (2013). Recursive Deep Models for Semantic CompositionalityOver aSentiment Treebank. 9. 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 onData Mining(ICDM) ,2016,1-10. https://doi.org/10.1109/ICDM.2016.0042. 10. Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep BidirectionalTransformers for Language Understanding. NAACL-HLT. 11. Shen, D., Hu, Y., & Sun, Y. (2018). Cross-lingual Sentiment Analysis: A Survey. http://www.internationaljournal.co.in/index.php/jasass 896 Volume15Issue03, March2025 Impact factor: 2019: 4.679 2020: 5.015 2021: 5.436, 2022: 5.242, 2023: 6.995, 2024 7.75 IEEE Access, 6,10506-10516. 12. Hovy, D., & Falk, C.(2017). Sentiment Analysis andIts Application in Language and Culture.Journal ofLinguistic Research. 13. Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations andTrends inInformation Retrieval, 2(1-2), 1-135. 14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, Ł., &Polosukhin,I. (2017). Attention is All You Need. NIPS. 15. G.S.Matlatipov. SENTIMENTAL ANALYSIS OF THE UZBEKISTAN LANGUAGE. Abstract. 16. Davronov, A. (2020). Application of Computational Linguistics and Sentiment Analysis intheUzbek Language. Uzbekistan Instituteof Scienceand Technology. 17. Karimov, M., & Tashpulatov, D. (2021). Computational Linguistics and Its Use in Different Languages: Practical Applications of Sentiment Analysis. International Scientific Developments 3(5), 44-53. http://www.internationaljournal.co.in/index.php/jasass 897