Abstract
In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.
Original language | English |
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Pages (from-to) | 42-49 |
Number of pages | 8 |
Journal | Knowledge-Based Systems |
Volume | 108 |
DOIs | |
State | Published - 15 Sep 2016 |
Keywords
- Aspect extraction
- CNN
- DNN
- Opinion mining
- RBM
- Sentiment analysis