Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2017

Abstract

Aspect identification is an important problem in opinion mining. It is usually solved in an unsupervised manner, and topic models have been widely used for the task. In this work, we propose a neural network model to identify aspects from reviews by learning their distributional vectors. A key difference of our neural network model from topic models is that we do not use multinomial word distributions but instead embedding vectors to generate words. Furthermore, to leverage review sentences labeled with aspect words, a sequence labeler based on Recurrent Neural Networks (RNNs) is incorporated into our neural network. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect quality, perplexity and sentence clustering results.

Keywords

Entropy, coherence, opinion mining, Aspect identifications, Different domains, Multinomials, Neural network model

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Advances in knowledge discovery and data mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, Proceedings

Volume

10235

First Page

668

Last Page

680

ISBN

9783319575292

Identifier

10.1007/978-3-319-57529-2_52

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-319-57529-2_52

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