Sentiment Analysis of E-Commerce Product Reviews

This paper presents a comprehensive literature review on the application of sentiment analysis in the context of e-commerce product reviews. It explores a wide range of traditional machine learning and advanced deep learning approaches used for extracting sentiment from customer feedback. The review highlights various models, datasets, evaluation metrics, and real-world applications, emphasizing their effectiveness in analyzing consumer opinions. Key challenges such as sarcasm detection, multilingual text, data imbalance, and domain adaptation are also discussed. The study aims to provide valuable insights for researchers and developers to enhance sentiment-based systems for better decision-making in e-commerce platforms.Sentiment Analysis (SA) of e-commerce product reviews plays a crucial role in understandingconsumer opinions and shaping business strategies. This paper provides a comprehensive review ofmethods used in SA for e-commerce reviews, focusing on traditional machine learning techniques likeSupport Vector Machines (SVM) and Naive Bayes, as well as advanced deep learning models such asConvolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. We exploredata preprocessing techniques, model accuracy, and challenges such as handling imbalanced data andsarcasm. The paper also discusses the applications of sentiment analysis in areas like productrecommendations and customer feedback analysis. Finally, we identify future research directions,including improving model generalization and interpretability. This review offers valuable insights intothe current state of sentiment analysis in e-commerce