Thursday 19 Jan 2012Weakly-supervised Joint Sentiment-Topic Detection from Text

Dr. Chenghua Lin - Knowledge Media Institute, The Open University

Harrison 170 15:00-16:00

Sentiment analysis or opinion mining aims to use automated tools to
detect subjective information such as opinions,
attitudes, and feelings expressed in text. This paper proposes a novel
probabilistic modeling framework called joint sentiment-topic
(JST) model based on latent Dirichlet allocation (LDA), which detects
sentiment and topic simultaneously from text. A reparameterized
version of the JST model called Reverse-JST, obtained by reversing the
sequence of sentiment and topic generation in the modelling
process, is also studied. Although JST is equivalent to Reverse-JST
without a hierarchical prior, extensive experiments show that when
sentiment priors are added, JST performs consistently better than
Reverse-JST. Besides, unlike supervised approaches to sentiment
classification which often fail to produce satisfactory performance when
shifting to other domains, the weakly-supervised nature of JST
makes it highly portable to other domains. This is verified by the
experimental results on datasets from five different domains where
the JST model even outperforms existing semi-supervised approaches in some
of the datasets despite using no labelled documents.
Moreover, the topics and topic sentiment detected by JST are indeed
coherent and informative.

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