The rapid development of digital technologies has significantly transformed e-commerce platforms, allowing users to interact and share opinions about products and services. Customer reviews play a crucial role in assessing product quality and guiding purchase decisions. However, due to the subjective nature and large volume of feedback, analyzing consumer opinions is complex and requires automated models. This study proposes a model for evaluating customer satisfaction using digital technologies, incorporating numerical ratings, likes/dislikes, and textual sentiment and topic analysis. Data collected from Alibaba reviews of Mac, Windows Surface, Asus, and Samsung laptops were analyzed using Principal Component Analysis (PCA) to determine factor weights and overall satisfaction scores. The proposed approach enables businesses to identify product strengths and weaknesses, improve service quality, enhance marketing strategies, and strengthen brand–customer relationships. Integrating sentiment analysis with AI and chatbots also promises more personalized customer interactions and higher satisfaction in digital commerce.

E-commerce, Customer Feedback, Sentiment Analysis, Principal Component Analysis (PCA), Digital Technologies, Review Normalization, Consumer Satisfaction, AI Chatbots

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