In today’s interconnected world, the pace of change is increasing gradually and the effects of an event can propagate and disrupt industries, organizations or companies more dramatically and quickly. Therefore, having a comprehensive overview of the environment is a precious asset for resilience and sustainable growth. One enabler of the above-mentioned interconnectedness is the rapid flow and vast availability of information in text form, which can be also used as the fundamental resource to understand the shifting environment. Hence, actors can be able to become aware of changes at an early stage. The underlying patterns to filter relevant information can be detected by learning from data, or more specifically machine learning. Natural language processing (NLP) techniques can be applied because text data is analyzed. However, to embed the expertise and perspective of the user into the initial model, data should be labeled. This requires valuable expert time from the organization for the labeling, thus it should be minimized. This study aims to present an efficient and user-friendly solution for data labeling. To achieve this, a modularized Active Learning-based backend is combined with an intuitive interface. The output of this labeling process will be used further to train a model for environment analysis. Nevertheless, the main focus of this paper is the development of a solution to maximize efficiency during data labeling for environment analysis. After an introduction to the problem, the overview of the suggested solution accompanied by a prototype will be demonstrated.