For effective research management, it is essential to have adequate knowledge of research resources and the needs of researchers. This study aims to propose a solution based on the content analysis of theses and user searches to identify research gaps by utilizing text miningText mining and natural language processingNatural language processing techniques, independent of expert opinions. To this end, over 25,000 thesis records and more than 440,000 search queries in Iran's scientific repository were analyzed as data related to the energyEnergy field. Bibliographic analysis of this data revealed that the supply and demand for these resources have grown significantly since the late 2000s. After extracting topics in this field using topic modelingTopic modeling, an analysis of the distance between topics on the supply and demand sides in Iran indicated that "environment" and "energyEnergy management" are hot topics, "oil" and "gas" are cold topics, and "technology," "energy economics," "renewable energy," and "electricity" are silent topics. Meanwhile, "solar energy" represents a research gap in the energy field. This study can assist research policymakers in making significant research decisions and prioritizing research in the energyEnergy field. Additionally, due to its interdisciplinary approach, researchers in fields such as information technology, artificial intelligenceArtificial intelligence, librarianship, information science, and others can utilize this approach to identify research gaps in their respective areas of interest.