Understand customer reviews with less data and in short time: pretrained language representation and active learning

ABSTRACT In this paper, we address customer review understanding problems by using supervised machine learning approaches, in order to achieve a fully automatic review aspects categorisation and sentiment analysis. In general, such supervised learning algorithms require domain-specific expert knowledge for generating high quality labeled training data, and the cost of labeling can be very high. To achieve an in-production customer review machine learning enabled analysis tool with only a limited amount of data and within a reasonable training data collection time, we propose to use pre-trained language representation to boost model performance and active learning framework for accelerating the iterative training process. The results show that with integration of both components, the fully automatic review analysis can be achieved at a much faster pace. Index T erms -- deep neural networks, natural language processing, embedding, active learning, sentiment analysis, multi-aspect classification 1. INTRODUCTION Natural language processing has gain continuously attention in recent years, not only for academe research purposes but also for a real-world use case in various industrial sectors.