Project Description
Qualitative Content Analysis (QCA) involves techniques to understand text content, applied in diverse areas like survey responses, social media, and online reviews. However, QCA is labor-intensive, deterring researchers from open-ended text analysis due to resource investments. Topic models like Latent Dirichlet Allocation (LDA) emerged as automated text categorization, but their variable use and perceived inadequacies challenge their effectiveness. Addressing this, TOPCAT integrates automated topic modeling with manual analysis, aligning with the qualitative content analysis process. Unlike previous human-centered approaches to topic modeling, TOPCAT prioritizes widespread utility for qualitative researchers, aiming to enhance efficiency and reliability in text analysis.
