Abstract
Sentiment analysis, also named opinion mining, is an important task in e-commerce. Recent years, many researchers have been focused on fine-grained sentiment analysis. Aspect level opinion mining detects the detailed sentiments about features of products. However, current aspect identification methods mainly focus on extracting explicit appeared aspects. The task of implicit aspect identification is still a big challenge in sentiment analysis. In this paper, we propose a novel implicit aspect identification approach based on non-negative matrix factorization. The approach first clusters product aspects by combining the co-occurrence information with intra-relations of aspect and opinion words, which can enhance the performance of aspect clustering substantially. In the next step, the approach collects context information of aspects, and represents review sentences by word vectors. Finally, a classifier is constructed to identify and predict the target implicit aspects. We also prove the convergence of our approach. Experimental results demonstrate that our approach outperforms baseline methods in most cases.
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Notes
The code and data are publicly available at https://github.com/nan0606/implicit-aspect-identification.
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Acknowledgements
The work described in this paper was partially support by National Natural Science Foundation of China (Project No. 61373046) and Natural Science Basic Research Plan in Shaanxi Province of China (Project No. S2015YFJM2129).
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Xu, Q., Zhu, L., Dai, T. et al. Non-negative matrix factorization for implicit aspect identification. J Ambient Intell Human Comput 11, 2683–2699 (2020). https://doi.org/10.1007/s12652-019-01328-9
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DOI: https://doi.org/10.1007/s12652-019-01328-9