Detection of potential customers' empathy behavior towards customers' reviews

The aim of the current study is to identify potential customers' empathy behavior and their behavioral reactions based on appraisal and stimulus-organism-response (SOR) theories to customers' reviews of financial services firms using lexicon-based unsupervised learning techniques. After filtering, we obtained 30263 reviews from the Yelp dataset of financial service companies. We examined the connections between several sorts of emotional dimensions and different types of behavioral reactions of potential consumers using lexicon-based unsupervised machine learning methods. Our findings show that the various types of customer sentiment have a significant impact on potential customers' emotional experiences on social media platforms, prompting them to behave differently. Furthermore, potential consumers' reactions to the customers' reviews varied according to their seven emotional aspects. The study is the first to examine the impact of potential customers' empathetic behavioral reactions on customers' evaluations using lexicon-based unsupervised learning techniques.

Automated summary

The aim of this study is to analyze customers' empathy behavior and their behavioral reactions to financial services firms using lexicon-based unsupervised learning techniques. The findings indicate that customer sentiment significantly affects potential customers' emotional experiences and their reactions to customer reviews vary based on different emotional aspects.