Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

In this investigation, we have shown that the combination of deep learning, including natural language processing, and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions, i.e. the model and the test set are from the same review category or cross-category predictions, i.e. using a model of another review category for predicting the test set. The similar results from in- and cross-category predictions indicate high degree of generalizability across product review categories. The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.

Automated summary

In this study, the combination of deep learning and conformal prediction was used to predict the sentiment of Amazon product reviews. The results showed high accuracy and efficiency in predicting the sentiment of the test set, both within the same category and across different categories. Additionally, the combination of deep learning and conformal prediction was found to handle class imbalances without explicit class balancing measures.