  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.
