In this presentation, I explain how we develop a sentiment analysis model using Bert-transformer. This sentiment model is developed to classify employee’s comments from different surveys within ING. We use a novel method to compare model’s outputs continuously in order to monitor the model’s performance. Simultaneously, by using active learning, the algorithm proactively flags comments which is not confident enough for labeling. Therefore, this method ensure that the annotators only annotates the most important and difficult comments, thus making the whole process more efficient and boost model performance.