This paper presents a seven-stage text analytics pipeline applied to 69,110 Samsung product reviews to evaluate the effectiveness of customer-generated reviews as determinants of brand performance. The methodology integrates lexicon-based sentiment classification, Term Frequency-Inverse Document Frequency (TF-IDF) feature engineering, Latent Dirichlet Allocation (LDA) topic modelling with seven latent topics, and a Logistic Regression classifier achieving a Receiver Operating Characteristic Area Under Curve (ROC-AUC) of 0.980 and 94.3% classification accuracy. Results indicate that 58.6% of reviews carry positive sentiment, yielding a Net Promoter Score (NPS) proxy of +38.2. Topic modelling reveals software performance and battery charging as primary pain-point domains, recording mean star ratings of 3.71 and 3.74 respectively. A monotonic positive relationship is observed between product price tier and all satisfaction metrics. Findings demonstrate that scalable NLP pipelines constitute a viable and operationally actionable framework for continuous brand health monitoring in competitive consumer electronics markets.
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