Application of the Naive Bayes Method for Sentiment Analysis of Sunscreen Product Reviews Based on the Female Daily Review - Melisa Nur Aini; Rita Yulfani; Nurul Jariah (Cattleya Darmaya Fortuna, 2024)
This study aims to apply the Naïve Bayes method for sentiment analysis of sunscreen product reviews based on data collected from the Female Daily platform. With the exponential growth of e-commerce, online reviews have become a valuable source of information for consumers seeking insights into product quality and user satisfaction. Sentiment analysis, a branch of natural language processing, plays a crucial role in extracting sentiments or opinions from text data. In this research, we focus specifically on sunscreen products and leverage the Naïve Bayes classifier to classify the sentiment polarity (positive, negative, or neutral) of reviews gathered from the Female Daily platform. The Female Daily platform provides a wealth of user-generated content, including detailed product reviews and ratings, making it an ideal dataset for sentiment analysis. By implementing the Naïve Bayes method, which is known for its simplicity and efficiency in text classification tasks, we aim to accurately identify sentiments expressed in sunscreen product reviews. The findings of this study are expected to contribute to the enhancement of consumer decision-making processes by providing valuable insights into the sentiment trends surrounding sunscreen products, ultimately aiding consumers in making informed purchasing decisions.