USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR SENTIMENT ANALYSIS OF USER OPINIONS ON SOCIAL NETWORKS

Authors

  • D.I. O’skanova Master’s student, University of Exact and Social Sciences 2nd year student, Tashkent, Uzbekistan

Keywords:

Artificial Intelligence, Sentiment Analysis, NLP, Deep Learning, BERT, Social Media.

Abstract

This article explores the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies in analyzing user opinions on social networks. The study focuses on the implementation of deep learning models such as LSTM, BERT, and transformer-based algorithms for sentiment analysis (positive, negative, neutral). The process of collecting, cleaning, and analyzing text data from various social media platforms (Twitter, Telegram, Instagram) is discussed in detail. Evaluation metrics such as accuracy, precision, and recall are analyzed to assess the performance of sentiment detection models.

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References

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

Liu, Y., Ott, M., Goyal, N. et al. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Pang, B., Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval.

Haddi, E., Liu, X., Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis.

Mikolov, T. et al. (2013). Efficient Estimation of Word Representations in Vector Space.

OpenAI (2023). Sentiment Classification with GPT-based models.

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Published

2025-10-31

How to Cite

D.I. O’skanova. (2025). USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES FOR SENTIMENT ANALYSIS OF USER OPINIONS ON SOCIAL NETWORKS. Journal of Applied Science and Social Science, 15(10), 1594–1596. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/2281

Issue

Vol. 15 No. 10 (2025): Volume 15 Issue 10

Section

Articles

License

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