Analyzing Methods for Classification of Electronic Word-of-Mouth: A Review
Gabriel Kanev, Tsvetelina Mladenova, Irena Valova · 2024
This paper explores the evolving landscape of electronic word-of-mouth (eWOM) and its impact on consumer behavior. With the transition from traditional word-of-mouth to online platforms (users’ reviews), businesses now have access to vast amounts of user-generated data. This publication reviews methodologies for sentiment analysis (SA) and possibilities for automatic classification, key approaches in understanding consumer opinions, and discusses some of the most notable methods and practices. Additionally, it examines the preprocess of the reviews and the steps that should be done before the classification. Finally, future research directions include personalization of ratings, integration with social networks, and addressing challenges related to fake reviews are mentioned. Embracing these advancements can help businesses leverage eWOM to inform marketing strategies and enhance consumer experiences.