Unveiling Emotional Intensity in Online Reviews: Adopting Advanced Machine Learning Techniques

Abstract

<jats:p> The digital revolution has spurred significant growth in online reviews and user-generated content. Traditional methods used in Marketing for analysing large datasets have limitations, emphasising the need for improved analytical approaches, particularly with the advent of artificial intelligence technology. This research used a state-of-the-art transformer model to analyse extensive online book reviews to accurately identify six specific emotions in the reviews of both fiction (hedonic) and nonfiction (utilitarian) genres. This study collected 3,157,703 reviews of 15,293 books voted ‘best book of the year’ on GoodReads.com over the past decade. Our findings reveal noticeable differences in emotional intensity across genres, with nonfiction displaying a slightly higher level of joy, and fiction showing higher levels of anger, sadness and surprise. Joy emerged as the dominant emotion across genres; however, it does not necessarily have a direct impact on book ratings. This study emphasises the intricacies of reader emotions, serving as a significant case study for marketers and publishers aiming to optimise their strategies in the contemporary literary market. The study contributes to the literature on the impact of consumers’ emotional responses, how they are reflected in social review commentary for high-involvement online products, and their impact on product ratings. </jats:p>