Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model

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Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model
R. Delshi Howsalya Devi^1,*, S.Prabu² and N. Legapriyadharshini³
¹Professor & Head, Department of Artificial Intelligence and Data Science, Karpaga Vinayaga College of Engineering and Technology, Maduranthagam Taluk, Tamil Nadu, 603308, India.
²Assistant Professor, Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India.
³Associate Professor, Department of Computer Applications, Saveetha College of Liberal Arts and Sciences (SIMATS), Chennai, Tamil Nadu,600124, India.
^*Corresponding Author Name: R. Delshi Howsalya Devi. Email: delshi@rocketmail.com
Journal of Computer Allied Intelligence(JCAI),30 June 2024,2(3),28-40
Received: 10 May 2024 Accepted: 20 June 2024 Published: 30 June 2024

This paper investigates the integration of the Mamdani Fuzzy Regression for User Experience (MFR-UE) model with deep learning techniques to enhance predictive analytics in e-commerce platforms. Through empirical analysis, the study evaluates the effectiveness of this hybrid approach in classifying customer preferences and sentiments based on complex feature sets. Results from demonstrate the model’s capability: Sample ID 1, characterized by Feature 1: 0.8, Feature 2: 0.6, Feature 3: 0.4, and Feature 4: 0.2, accurately predicts Class A as both the Predicted Class and Actual Class. Similarly, Sample ID 4, with Feature 1: 0.1, Feature 2: 0.4, Feature 3: 0.3, and Feature 4: 0.8, correctly identifies Class C. The integration leverages deep learning’s capacity to discern intricate data patterns alongside MFR-UE’s fuzzy logic for nuanced decision-making, optimizing business strategies and enhancing user experience. Practical implications include refined customer segmentation, personalized marketing strategies, and improved service delivery, emphasizing the model’s potential for driving competitive advantage in e-commerce.
Keywords: Sentimental Analysis; E-commerce; User Experience; Classification; Fuzzy Logic.
Citation : R. Delshi Howsalya Devi ,S.Prabu and N. Legapriyadharshini “Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model”, Journal of Computer Allied Intelligence (JCAI), vol.02, no.03, pp.28-40,2024.