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2017 | OriginalPaper | Buchkapitel

A Neural Network Model for Semi-supervised Review Aspect Identification

verfasst von : Ying Ding, Changlong Yu, Jing Jiang

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Multi-view Regularized Gaussian Processes

Nächstes Kapitel Unsupervised Embedding for Latent Similarity by Modeling Heterogeneous MOOC Data

https://code.google.com/archive/p/word2vec/.

https://www.yelp.com.sg/dataset_challenge.

http://www.cs.virginia.edu/~hw5x/dataset.html.

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Titel: A Neural Network Model for Semi-supervised Review Aspect Identification
verfasst von: Ying Ding
Changlong Yu
Jing Jiang
Verlag: Springer International Publishing
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-57528-5

Electronic ISBN: 978-3-319-57529-2

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-57529-2_52

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