Broadcasting companies produce large amounts of text and audiovisual content. Extracting meaningful insights from these sources requires efficient analysis methods, which are often only palatable to data scientists. Even in large organizations, there is a critical knowledge gap: media experts manually curate work to derive insights, which is very time consuming, while engineers can use advanced data science methods but lack the domain expertise to derive key insights from the data. We propose to bridge this knowledge gap with INTEX, a human-in-the-loop interactive topic modeling application. We designed INTEX considering non-technical media experts as the main stakeholders of the application. A user evaluation shows that INTEX enables domain experts to extract and explore topics in an intuitive and efficient manner. Our work illustrates how complex applications can be made more accessible by hiding low-level details and linking these to high-level interpretations. INTEX overcomes past challenges in topic modeling, representing the future of interactive applications in this domain.