aspg-18-4176-1793779999 20261104081319 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 17 2 N. B. Mahesh Kumar Associate Professor, Department of Computer Science and Engineering, Hindusthan Institute of Technology, India Subbulakshmi M. PG Scholar, Department of Computer Science and Engineering, Hindusthan Institute of Technology, India T. Baranidharan Professor, Department of Electronics and Communication Engineering, K.S.Rangasamy College of Technology, India Mohana Sundharam M. Assistant Professor, Computer Science and Engineering, Hindusthan College of Engineering and Technology Valley Campus, Pollachi Road, Coimbatore, India Geetha M. P. Assistant Professor, Department of Artifical Intelligence and Data Science, Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India 16.00 Normal false false false TR X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" Name="toc 3"/> 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mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi; mso-ansi-language:TR;} Traditional recommendation systems primarily rely on user behavior, ratings, and content-based preferences to suggest products or services. However, they often overlook the nuanced emotional context that significantly influences consumer decision-making. This paper proposes a Sentiment-Enhanced Recommendation System (SERS) that integrates sentiment analysis with collaborative and content-based filtering to better capture the affective dimensions of user preferences. By analyzing user-generated content such as reviews, comments, and social media posts using deep learning-based sentiment classifiers, the proposed model quantifies emotional polarity and intensity. These sentiment signals are then incorporated into the recommendation pipeline using hybrid matrix factorization and attention mechanisms, enabling dynamic adaptation to users' emotional states. Experimental evaluations conducted on datasets from Amazon and Yelp demonstrate significant improvements in precision, recall, and user satisfaction scores compared to traditional models. The findings highlight the critical role of emotions in shaping consumer behavior and underscore the importance of affect-aware personalization in modern recommendation systems. 2025 404 414 4176 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.170226 https://americaspg.com/journal/18/article/4176 https://americaspg.com/storage/81762449283.pdf