Understand customer reviews with less data and in short time: pretrained language representation and active learning
This work addresses the high cost and time of labeling data for businesses needing efficient customer review analysis, though it is incremental as it builds on existing methods.
The paper tackled the problem of automating customer review analysis by combining pre-trained language representations with active learning, achieving fully automatic review categorization and sentiment analysis with significantly reduced data and training time.
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.