What is Ensembles Learning Technique

Electronic word of mouth (e-WOM) is rapidly becoming an empowering tool for consumers to express their experiences on services or products, on social media or other platforms. Beyond the obvious implications of such content to potential consumers, interest is also high among researchers, industry players, and other stakeholders who strive to analyze before-and-after sales expectations, emotions, and perceptions of customers. The need to find efficient ways of extracting and then analyzing online content rendered the reuse of tools and methodologies initially applied in other fields as well as the development of new approaches. In this chapter, the authors identify high-impact scientific work related to e-WOM and point out the analytical methods for analyzing e-WOM content. Furthermore, this chapter refers to the most relevant studies employing such methods and their findings. More specifically, it discusses clustering, sentiment analysis, supervised and unsupervised machine learning, lexicon-based approaches, corpus-based approach, summarization and predicting, and regression analysis.