Using BERT with Different Deep Learning Techniques to Classify Reviews

Every day, the volume of textual information is growing at an accelerated rate. A portion of this data is presented as product, movie, game, and other review content. These reviews fall into two general categories: favourable and unfavourable. Since there are a lot of reviews for popular products, a classifier that can categorise reviewers as positive or negative is necessary. This work compares the performance of BERT with other widely used approaches for review classification. Specifically, we combine BERT with several deep learning algorithms (BiGRU, 1-D CNN, and GRU-CNN). Using a pre-trained BERT model, we first turn the content into numerical vectors. Next, we feed the embeddings into several models and evaluate how well they perform. We evaluate the performance based on f1 macro, accuracy, precision, and recall and find that the BERT+CNN_GRU model outperforms the widely-used models for review classification.