Abstract
Since the past few years, so-called pre-trained Language Models (PLM) are considered state-of-the-art in the field of Natural Language Processing (NLP) and are thus experiencing widespread and successful application. In addition to traditional supervised Machine Learning (ML) tasks such as spam email or customer churn classification, this technology opens up advanced approaches to unsupervised learning and data analytics in general. One of particular interest is the automatic identification of latent topics within a large collection of texts, also known as Topic Modeling (TM). Such modelling approaches offer great potential, especially for industrial environments as well as the consumer goods market, to explore increasing amounts of data from diverse and constantly growing data sources. As a holistic concept, it can be utilized in a highly targeted and efficient manner for applications through an optimized combination of Artificial Intelligence (AI) and Cloud Computing (CC) systems.
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