Using natural language processing and machine learning, we discover emergent themes and patterns within our dialogue data set. Exploring how those relate to human-generated themes and patterns, we can identify how various means of sensemaking relate to one another. Further, by analyzing the dialogue as a whole, we are able to identify relationships between themes temporally and visualize how conversations unfold overtime to help interpret and understand conversations and communities.