Intelligent methods for analyzing veracity and helpfulness of online reviews

This involves performing individual rankings for each characteristic, which are then amalgamated into a final helpfulness ranking. Empirical evaluations on Amazon product reviews validate the effectiveness of this approach against a contemporary baseline. Additionally, this thesis advocates a multi-task learning approach to simultaneously tackle fake reviews detection and review helpfulness prediction.

Both tasks use the same features with fake reviews and helpfulness prediction and employ five deep learning models. By integrating these tasks, shared information among features is leveraged to enhance the performance of each task. Pre-trained RoBERTa embeddings are utilized for all data representations, and ensemble learning techniques are employed to improve prediction accuracy and reduce the risk of overfitting .The combination of different data representations, especially when fused using early and late data fusion techniques, significantly outperforms single data representation models.

The findings from this research not only advance the field of NLP and machine learning but also provide practical methodologies to enhance the reliability and utility of online review platforms. These comprehensive approaches to tackling the twin challenges of fake reviews detection and review helpfulness prediction offer valuable insights and tools for both consumers and retailers in the digital marketplace.