AI-Powered Product Innovation Using Natural Language Processing for Customer Feedback and Market Intelligence

Abstract

Digital customer reviews contain valuable information about product experiences, preferences, and expectations, but their unstructured nature makes large-scale analysis challenging. Artificial intelligence (AI) and natural language processing (NLP) provide opportunities to transform customer-generated text into structured information for product innovation and market intelligence. This study aimed to evaluate NLP-based machine-learning models for classifying customer reviews according to product category and to identify recurring customer-feedback themes that could provide product-innovation and market-intelligence insights. The study used the UCI Machine Learning Repository. Customer-review text was preprocessed and represented using term frequency-inverse document frequency (TF-IDF) features. Logistic Regression, Linear Support Vector Machine, and Complement Naive Bayes were evaluated using an 80:20 stratified training-testing split. Accuracy, precision, recall, and F1-score were used for comparative evaluation. Non-negative Matrix Factorization was subsequently applied to identify recurring topics within the review corpus. All three classification models demonstrated effective product-category classification, with Complement Naive Bayes achieving the strongest overall performance among the evaluated approaches. Category-level analysis showed variation in classification performance across product domains. Topic modeling identified recurring themes related to price and value, product quality, usability, appearance, recommendations, delivery experiences, and product-specific characteristics. These findings indicate that customer reviews contain multiple dimensions of information relevant to product-oriented analysis. Combining supervised NLP classification with topic modeling can transform unstructured customer reviews into structured customer-derived information relevant to product innovation and market intelligence.