Sentimental Analysis for the Improved User Experience in the E-Commerce Platform with the Fuzzy Model
DOI:
https://doi.org/10.69996/jcai.2024013Keywords:
Sentimental Analysis, E-commerce, User Experience, Classification, Fuzzy LogicAbstract
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.
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