Concurrent Visualization of Relationships between Words and Topics in Topic Models
Alison Smith, Jason Chuang, Yuening Hu, Jordan Lee Boyd-Graber, Leah Findlater · 2014
Analysis tools based on topic models are often used as a means to explore large amounts of unstructured data. Users of-ten reason about the correctness of a model using relationships between words within the topics or topics within the model. We compute this useful contextual informa-tion as term co-occurrence and topic co-variance and overlay it on top of stan-dard topic model output via an intuitive interactive visualization. This is a work in progress with the end goal to combine the visual representation with interactions and online learning, so the users can di-rectly explore (a) why a model may not align with their intuition and (b) modify the model as needed. 1