aspg-3-2562-1791417578 20261007235938 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 1 B. Shanthini Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India N. Subalakshmi Department of Computer and Information Science, Annamalai University, Chidambaram, Tamil Nadu, India The analysis of sentiment in product reviews across diverse platforms such as e-commerce website and social media presents a challenging task due to the inherent differences in user behaviour and review formats. This research introduces an innovative methodology for detecting positive and negative deviations in cross-domain product reviews using Adaptive Stochastic Deep Networks (ASDN) tailored for multi-platform sentiment analysis. ASDNs possess mechanisms that enable dynamic adaptation to changes in data distributions, domain shifts, or varying complexities within the input data. The proposed framework aims to capture refined variations in sentiment expression across disparate platforms by incorporating adaptive stochasticity within deep neural networks. By adapting dynamically to changes in review styles, language use, and sentiment patterns unique to each platform, the ASDN architecture facilitates the identification of nuanced sentiment shifts. Through extensive experimentation on comprehensive datasets spanning Amazon, Facebook, and Instagram, the efficacy of the ASDN model in detecting positive and negative sentiment deviations across diverse platforms is demonstrated. This research contributes to advancing the understanding of sentiment dynamics across distinct social platforms and e-commerce sites, paving the way for more robust and adaptable models in cross-domain sentiment analysis. 2024 128 143 2562 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150111 https://www.americaspg.com/journal/3/article/2562 https://www.americaspg.com/storage/91709805045.pdf