A Novel Approach Based on Information Relevance Perspective and ANN for Predicting the Helpfulness of Online Reviews

This study presents a novel approach to predicting the helpfulness of online reviews using Artificial Neural Networks (ANNs) focused on information relevance. As online reviews significantly influence consumer decision-making, it is critical to understand and identify reviews that provide the most value. This research identifies four key textual features namely content novelty, content specificity, content readability, and content reliability, that contribute to perceived helpfulness and incorporates them as primary inputs for the ANN model. Datasets of Amazon reviews are analyzed, and various preprocessing steps are employed to ensure data quality. Reviews are classified as helpful or unhelpful based on helpful vote thresholds, with experiments conducted across multiple helpful vote thresholds to determine the optimal threshold value. Performance was evaluated using accuracy, precision, recall, and F1 scores, with the best-performing classifier achieving 74.34% accuracy at a helpful vote threshold of 12 votes. These results highlight the potential of information relevance-based criteria to enhance the accuracy of online review helpfulness prediction models.