In recent years, there has been growing interest in using NLP tools for decision support systems, particularly in Science, Technology, and Innovation (STI). Among these, topic modeling has been widely used for analyzing large document collections, such as scientific articles, research projects, or patents, yet its integration into decision-making systems remains limited. This paper introduces CASE, a tool for exploiting topic information for semantic analysis of large corpora. The core of CASE is a Solr engine with a customized indexing strategy to represent information from Bayesian and Neural topic models that allow efficient topic-enriched searches. Through ad-hoc plug-ins, CASE enables topic inference on new texts and semantic search. We demonstrate the versatility and scalability of CASE through two use cases: the calculation of aggregated STI indicators and the implementation of a web service to help evaluate research projects.

Topic Modeling is a commonly used technique for analyzing unstructured data in various fields, but achieving accurate results and useful models can be challenging, especially for domain experts who lack the knowledge needed to optimize the parameters required by this natural language processing technique. From this perspective, we introduce an Interactive Topic Model Trainer (ITMT) developed within the EU-funded project IntelComp. ITMT is a user-in-the-loop topic modeling tool presented with a graphical user interface that allows the training and curation of different state-of-the-art topic extraction libraries, including some recent neural-based methods, oriented toward the usage by domain experts. This paper reviews ITMT’s functionalities and key implementation aspects in this paper, including a comparison with other tools for topic modeling analysis.