Reviewtag: Tagging Amazon Negative Product Review with Deep Learning

The success of Amazon sellers hinges on high ratings and meeting customer needs with exceptional products and services. However, the large scale of negative reviews pose significant challenges that require careful analysis to identify underlying reasons of buyers concerns. We aim to develop an automated tagging system named ReviewTag to address this challenge. The system uses deep learning models and Natural Language Processing (NLP) techniques to swiftly categorize negative reviews into two broader categories i.e., product issues and seller issues. The system provides further insight into customers' specific issues using subtopic tagging, allowing Amazon sellers to identify areas for improvement and make data-driven decisions to meet evolving customer expectations. We employ five deep learning models to perform topic and subtopic tagging. These models include Bidirectional Encoder Representations from Transformers (BERT), Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Based on the evaluation with a prototype implementation of ReviewTag, the BERT model demonstrates high precision, recall, and F1-scores of 0.97, 0.96, and 0.96, respectively, for topic tagging. Additionally, the BERT and CNN models show impressive precision, recall, and F1-scores of about 0.92, for subtopic tagging. These results demonstrate the effectiveness of deep learning models for automatically tagging negative product reviews on Amazon. It helps Amazon sellers take action to improve their product ratings.