One of the overarching tasks of document analysis is to find what topics people talk about. One of the main techniques for this purpose is topic modeling. So far many models have been proposed. However, the existing models typically per-form full analysis on the whole data to find all topics. This is certainly useful, but in practice we found that the user almost always also wants to perform more detailed analyses on some specific aspects, which we refer to as targets (or targeted aspects). Current full-analysis models are not suitable for such analyses as their generated topics are often too coarse and may not even be on target. For example, given a set of tweets about e-cigarette, one may want to find out what topics under discussion are specifically related to children. Likewise, given a collection of online reviews about a camera, a consumer or camera manufacturer may be interested in finding out all topics about the camera’s screen, the targeted aspect. As we will see in our experiments, current full topic models are ineffective for such targeted analyses. This paper studies this problem and proposes a novel targeted topic model (TTM) to enable focused analyses on any specific aspect of interest. Our experimental results demonstrate the effectiveness of the TTM.


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Filed under: Dimensionality Reduction