A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics

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A Two-Dimensional Topic-Aspect Model for Discovering Multi-Faceted Topics
This paper presents the Topic-Aspect Model (TAM), a Bayesian mixture model which jointly discovers topics and aspects. We broadly define an aspect of a document as a characteristic that spans the document, such as an underlying theme or perspective. Unlike previous models which cluster words by topic or aspect, our model can generate token assignments in both of these dimensions, rather than assuming words come from only one of two orthogonal models. We present two applications of the model. First, we model a corpus of computational ics abstracts, and find that the scientific topics identified in the data tend to include both a computational aspect and a linguistic aspect. For example, the computational aspect of GRAMMAR emphasizes parsing, whereas the linguistic aspect focuses on formal languages. Secondly, we show that the model can capture different viewpoints on a variety of topics in a corpus of editorials about the Israeli-Palestinian conflict. We show both qualitative and quant...
Michael Paul, Roxana Girju
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where AAAI
Authors Michael Paul, Roxana Girju
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