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A comprehensive review of probabilistic and statistical methods in social network sentiment analysis
- 1 University College London
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Abstract
In the era of rapid digital transformation, social networks generate huge amounts of textual data every day, making sentiment analysis an essential tool for understanding public opinion. This study focuses on the application of probabilistic and statistical methods to sentiment analysis in social networks, highlighting their effectiveness in dealing with uncertainty and modeling the distribution of emotions. The main objective is to evaluate the role of Naïve Bayesian (NB), Hidden Markov models (HMMs), and Bayesian networks in emotion classification, emotion propagation, and dynamic emotion tracking. Through literature review and comparative analysis, this study examines the existing research, computational efficiency, and real-world applications of probabilistic classification models. The results show that Naive Bayes is computationally efficient and effective for large-scale emotion classification, while HMM and Bayesian networks excel in sequential emotion prediction and user behavior modeling. The study highlights the advantages of probabilistic methods in sentiment analysis, while acknowledging their limitations, such as their reliance on probabilistic assumptions and the challenges of capturing deep contextual semantics. Future research should explore hybrid approaches that combine probabilistic models with deep learning techniques to improve the predictive performance and scalability of real-time sentiment analysis.
Keywords
sentiment analysis, naïve bayes, hidden markov models, latent dirichlet allocation
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Cite this article
Zheng,C. (2025). A comprehensive review of probabilistic and statistical methods in social network sentiment analysis. Advances in Engineering Innovation,16(3),38-43.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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