UCLA

This study investigates how individual sentiment towards product help another individual purchasing decision. It explores numerous sentiment analysis model such as Ordinal Regression, LSTM, Logistic Regression, Random Forest, and BERT, in predicting customer recommendations and ratings from Sephora product reviews. These findings demonstrate the models' ability to forecast customer recommendations consistently, offering significant insights that can enhance customer satisfaction, marketing initiatives, and inventory management. Despite the promising results, limitations, such as capturing the nuanced nature of evaluations and ensuring model generalizability, remain, emphasizing opportunities for future research.