Interactive Topic Modeling for aiding Qualitative Content Analysis

Topic Modeling algorithms are rarely used to support the qualitative content analysis process. The main contributing factors for the lack of mainstream adoption can be attributed to the perception that Topic Modeling produces topics of poor quality and that content analysts do not trust the derived topics because they are unable to supply domain knowledge and interact with the algorithm. In this paper, interactive Topic Modeling algorithms namely Dirichlet-Forrest Latent Dirichlet Allocation and Penalised Non-negative Matrix Factorisation, are evaluated with respect to their ability to aid qualitative content analysis. More specifically, the relationship between interactivity, interpretation, topic coherence and trust in interactive content analysis is examined. The findings indicate that providing content analysts with the ability to interact with Topic Modeling algorithms produces topics that are directly related to their research questions. However, a number of improvements to these algorithms were also identified which have the potential to influence future algorithm development to better meet the requirements of qualitative content analysts.