Publication Type

Conference Paper

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.

Discipline

Digital Communications and Networking | OS and Networks

Research Areas

Data Management and Analytics

Publication

Pacific-Asia Conference on Knowledge Discovery and Data Mining: PAKDD 2017: Advances in Knowledge Discovery and Data Mining

Identifier

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

Publisher

FT Prentice Hall

City or Country

Jeju

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

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

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