The growth of e-commerce platforms has produced large volumes of unstructured customer review text, motivating automated sentiment analysis. This paper presents a real-time sentiment analysis framework for Flipkart customer reviews that combines a multi-stage text preprocessing pipeline, comprising case normalization, URL and HTML removal, and Snowball stemming, with lexicon-based polarity scoring using NLTK’s VADER Sentiment Intensity Analyzer. The framework is evaluated on 2,304 customer reviews spanning 231 product listings, yielding an aggregate sentiment distribution of 60.0% positive, 11.7% negative, and 28.3% neutral, consistent with the dataset’s rating distribution. To address the absence of empirical validation in prior lexicon-based studies, the framework’s per-review classifications are benchmarked against rating-derived ground-truth labels using accuracy, precision, recall, and F1-score, and compared against the TextBlob lexicon-based library. Visual analytics, including donut charts and word clouds, support exploratory interpretation. The framework provides a scalable, validated, and reproducible baseline for real-time opinion mining on e-commerce review data.