Sentiment Analysis of Customer Product Review Using Machine Learning

Understanding customer opinions through sentiment analysis has become essential for businesses aiming to enhance customer satisfaction and refine their strategies. As digital platforms and e-commerce grow, consumers increasingly depend on online reviews for decision-making. This research delves into the analysis of customer product reviews using a range of sentiment analysis techniques, including lexicon-based approaches, traditional machine learning algorithms, and advanced deep learning models like Recurrent Neural Networks (RNN) and Transformer-based architectures such as BERT. The study emphasizes effective text preprocessing methods—such as tokenization, stemming, stopword elimination, and vectorization—to improve classification outcomes. Sentiment classification is assessed using metrics like accuracy, precision, recall, and F1-score to identify the most suitable model. Moreover, aspect-based sentiment analysis (ABSA) is employed to extract detailed opinions related to specific product features such as price, usability, durability, and customer service. The study also addresses challenges such as sarcasm detection, language diversity, and domain-specific interpretations. The insights from this research aim to support the development of intelligent sentiment analysis tools, enabling businesses to monitor feedback efficiently, tailor marketing strategies, and enhance customer experiences for long-term success.