Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach

Although online hotel reviews (OHR) help consumers in better decision-making, and service providers in better service design and delivery, they are hard to manage due to their high volume, velocity, and veracity. This paper focuses on the drivers of helpfulness of textual OHR, for which we have used text-mining techniques to find the sentiment content, polarity, and emotions; we have also used econometric and machine learning techniques to explain and predict its helpfulness. We found that content and title polarity lead to OHRs being less helpful, whereby this negative relationship gets accentuated with higher sentiment content. On the other hand, while negative emotion with low arousal makes OHR helpful, high arousal makes it less helpful. It has also been noted that after controlling for polarity, sentiment, and emotions, longer reviews are less helpful. Higher quantitative rating, recency of OHR and a reviewer's past expertise make a review more helpful. Additionally, machine-learning techniques have been found to predict 'review' helpfulness marginally better than econometric techniques. This study contributes to OHR literature in terms of its performance, and would also help decision makers in OHR management strategy.