sentiment classification

review helpfulness

Decision Support Systems

Online customer reviews (OCR) are an established source of competitive intelligence. Since OCR reflect perceptions of past users about product features and performance, prospective customers of mobile apps often resort to OCR before they decide to download an app. In addition to the textual content of the OCR, potential users of the app often consult the star ratings provided by the reviewers. In this paper, we determine if the star rating of mobile payment apps suitably captures the appropriate customer perception reflected in the textual content of the OCR for the payment apps. We conduct a three-dimensional analysis of 146,914 reviews from two popular mobile payment apps and uncover the sentiment and emotions captured in the review text, and the reactions in the emojis. Our analysis shows that the consolidated sentiment and the happiness emotion obtained from the OCR of mobile payment apps impacts the star ratings more strongly, than other emotions embedded in the text, and reactions in the emojis. The findings also hold good during the Covid-19 pandemic. We propose a novel perception score that captures the nuances in the OCR more effectively and suggest how mobile app providers can use it to arrange the OCR for better compatibility with content. The results of this research will be useful for mobile app providers that depend on OCR for popularizing their apps and will provide them a novel metric based on content, that can leverage OCR better to influence future downloads. • We analyze the textual content of 146,914 reviews of two mobile payment apps and their star ratings. • We conduct sentiment and emotion analysis of the review text and reaction analysis of the emojis. • The consolidated sentiment and the happiness emotion in reviews strongly impact their star ratings. • The proposed perception score closely reflects the emotions, sentiments and reactions embedded in the reviews.

Walking on air or hopping mad? Understanding the impact of emotions, sentiments and reactions on ratings in online customer reviews of mobile apps