FACULTY AND RESEARCH

The latest research shows that the identification of helpful reviews from a large volume of user–generated data is a trending topic. The present study uses an approach that not only predicts if an online review is helpful, neutral or not helpful with 66% accuracy, but most importantly models online review helpfulness. To this end, we use an adaptive implementation of 1D Convolutional Neural Networks (CNNs). The neuronal encoding of CNNs has the benefit of obtaining automatic data classification using cluster analysis to detect different types of clusters of helpful and not helpful reviews, categorized by their most important contextual characteristics. Findings reveal that the clusters with the most important words and documents for helpful reviews in the product category ‘Cars & Motorcycles’ describe cars and their characteristics, whereas not helpful reviews concern details about car-related shops/companies in general. By demonstrating high performance on prediction and classification of review helpfulness with our proposed methodology, we are contributing to the research on business intelligence. In addition, we provide significant practical implications for marketers, enabling them to distinguish between helpful and not helpful reviews. Using the resulting encoding can produce automatic data classification of different clusters of specific topics.