Abstract
With rapid development of E-commerce, a large amount of data including reviews about different types of products can be accessed within short time. On top of this, opinion mining is becoming increasingly effective to extract valuable information for product design, improvement and brand marketing, especially with fine-grained opinion mining. However, limited by the unstructured and causal expression of opinions, one cannot extract valuable information conveniently. In this paper, we propose an integrated strategy to automatically extract feature-based information, with which one can easily acquire detailed opinion about certain products. For adaptation to the reviews’ characteristics, our strategy is made up of a multi-label classification (MLC) for reviews, a binary classification (BC) for sentences and a sentence-level sequence labelling with a deep learning method. During experiment, our approach achieves 82% accuracy in the final sequence labelling task under the setting of a 20-fold cross validation. In addition, the strategy can be expediently employed in other reviews as long as there is an according amount of labelled data for startup.
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Foundation item: the National Natural Science Foundation of China (No. 61375053)
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Wang, Y., Wang, M. Fine-Grained Opinion Extraction from Chinese Car Reviews with an Integrated Strategy. J. Shanghai Jiaotong Univ. (Sci.) 23, 620–626 (2018). https://doi.org/10.1007/s12204-018-1961-6
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DOI: https://doi.org/10.1007/s12204-018-1961-6
Key words
- opinion extraction
- multi-label classification (MLC)
- binary classification (BC)
- sequence labelling
- recurrent neural network (RNN)