Concurrent Visualization of Relationships between Words and Topics in Topic Models

Abstract

Analysis tools based on topic models are often used as a means to explore large amounts of unstructured data. Users often reason about the correctness of a model using relationships between words within the topics or topics within the model. We compute this useful contextual information as term co-occurrence and topic covariance and overlay it on top of standard 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 directly explore (a) why a model may not align with their intuition and (b) modify the model as needed.

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Cite this paper

@inproceedings{Smith2014ConcurrentVO, title={Concurrent Visualization of Relationships between Words and Topics in Topic Models}, author={Alison Smith and Jason Chuang and Yuening Hu and Jordan L. Boyd-Graber and Leah Findlater}, year={2014} }