Publication Type
Conference Proceeding Article
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
Discipline
Digital Communications and Networking | OS and Networks
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
Citation
DING, Ying; YU, Changlong; and JIANG, Jing.
A neural network model for semi-supervised review aspect identification. (2017). Advances in knowledge discovery and data mining: 21st Pacific-Asia Conference, PAKDD 2017, Jeju, South Korea, May 23-26, Proceedings. 10235, 668-680. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3724
Creative Commons 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