Deep Learning in Electronic Word-of-Mouth: A Comprehensive Review and Future Directions

Electronic word-of-mouth (eWOM) has become a significant aspect of online communication, influencing consumer behavior and shaping brand perceptions. As the volume of user-generated content continues to grow exponentially, traditional methods of analyzing and understanding eWOM become increasingly challenging. This research paper provides a comprehensive review of the application of deep learning techniques in the context of eWOM. We explore the evolution of eWOM, the challenges associated with its analysis, and the role of deep learning in addressing these challenges. Additionally, we discuss key applications, methodologies, and current advancements in the field, along with potential future directions for research and development.