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A Hierarchical Aspect-Sentiment Model for Online Reviews
| Citations: | 13 - 3 self |
BibTeX
@MISC{Kim_ahierarchical,
author = {Suin Kim and Jianwen Zhang and Zheng Chen and Alice Oh and Shixia Liu},
title = {A Hierarchical Aspect-Sentiment Model for Online Reviews},
year = {}
}
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Abstract
To help users quickly understand the major opinions from mas-sive online reviews, it is important to automatically reveal the latent structure of the aspects, sentiment polarities, and the as-sociation between them. However, there is little work available to do this effectively. In this paper, we propose a hierarchical aspect sentiment model (HASM) to discover a hierarchical structure of aspect-based sentiments from unlabeled online reviews. In HASM, the whole structure is a tree. Each node it-self is a two-level tree, whose root represents an aspect and the children represent the sentiment polarities associated with it. Each aspect or sentiment polarity is modeled as a distribution of words. To automatically extract both the structure and pa-rameters of the tree, we use a Bayesian nonparametric model, recursive Chinese Restaurant Process (rCRP), as the prior and jointly infer the aspect-sentiment tree from the review texts. Experiments on two real datasets show that our model is com-parable to two other hierarchical topic models in terms of quantitative measures of topic trees. It is also shown that our model achieves better sentence-level classification accuracy than previously proposed aspect-sentiment joint models. 1
Keyphrases
sentiment polarity online review hierarchical aspect-sentiment model hierarchical topic model hierarchical structure whole structure unlabeled online review review text aspect-sentiment tree real datasets aspect-sentiment joint model topic tree latent structure recursive chinese restaurant process hierarchical aspect sentiment model sentence-level classification accuracy mas-sive online review aspect-based sentiment quantitative measure major opinion node it-self two-level tree bayesian nonparametric model little work
