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In this paper, we address customer review understanding problems by usingsupervised machine learning approaches, in order to achieve a fully automaticreview aspects categorisation and sentiment analysis. In general, suchsupervised learning algorithms require domain-specific expert knowledge forgenerating high quality labeled training data, and the cost of labeling can bevery high. To achieve an in-production customer review machine learning enabledanalysis tool with only a limited amount of data and within a reasonabletraining data collection time, we propose to use pre-trained languagerepresentation to boost model performance and active learning framework foraccelerating the iterative training process. The results show that withintegration of both components, the fully automatic review analysis can beachieved at a much faster pace.

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