Machine Learning for Quality Diagnostics: Insights into Consumer Electronics Evaluation

In the era of digital commerce, understanding consumer opinions has become crucial for businesses aiming to tailor their products and services effectively. This study investigates acoustic quality diagnostics of the latest generation of AirPods. From this perspective, the work examines consumer sentiment using text mining and sentiment analysis techniques applied to product reviews, focusing on Amazon’s AirPods reviews. Using the naïve Bayes classifier, a probabilistic machine learning approach grounded in Bayes’ theorem, this research analyzes textual data to classify consumer reviews as positive or negative. Data were collected via web scraping, following ethical guidelines, and preprocessed to ensure quality and relevance. Textual features were transformed using term frequency-inverse document frequency (TF-IDF) to create input vectors for the classifier. The results reveal that naïve Bayes provides satisfactory performance in categorizing sentiment, with metrics such as accuracy, sensitivity, specificity, and F1-score offering insight into the model’s effectiveness. Key findings highlight the divergence in consumer perception across ratings, identifying sentiment drivers such as noise cancellation quality and product integration. These insights underline the potential of sentiment analysis in enabling companies to address consumer concerns, improve offerings, and optimize business strategies. The study concludes that such methodologies are indispensable for leveraging consumer feedback in the rapidly evolving digital marketplace.