The Influence of Sentiment and Emotion on Helpful Reviews: Machine Learning Analysis of Emotion Dynamics in Online Reviews

Previous research on sentiment’s impact on perceived helpfulness shows mixed results; while some highlight the benefits of positive valence, others favour negativity or balanced (50/50) reviews. These inconsistencies may arise from sentiment polarity approaches that overlook emotional complexity. This study examines how sentiment and emotions expressed in online customer reviews on platforms such as TripAdvisor influence perceived helpfulness. We analysed the differences in three sentiments and eight emotions between helpful and unhelpful reviews (n = 2,785,999) using sentiment analysis (e.g., positive, neutral, and negative) and emotion analysis (e.g., anger, disgust, fear, joy, sadness, surprise, happiness, and love). To achieve this, we developed and trained an artificial intelligence emotion detection model using a transformer-based machine learning algorithm on a tweet emotion dataset (n = 2,774,566). Findings reveal that a slight increase in negative emotions (from 11% to 17%) significantly enhances perceived helpfulness, supporting negativity bias theory. These findings are further enriched by broader psychological theories such as emotional salience and diagnosticity, which help explain why certain emotional expressions in reviews may be more cognitively and behaviorally impactful. Reviews blending high positive and low negative emotions are most helpful, while extreme or balanced sentiments are less impactful. Additionally, negative emotions (notably sadness) are more prevalent in helpful reviews as price levels rise, suggesting an even stronger negativity bias. Logistic regression analysis further confirms emotion-focused models, particularly those emphasising negative emotions, exhibit greater explanatory power than sentiment-based models, particularly in the high-price context.