Abstract
Extracting aspects and sentiments is a key problem in sentiment analysis. Existing models rely on joint modeling with supervised aspect and sentiment switching. This paper explores unsupervised models by exploiting a novel angle – correspondence of sentiments with aspects via topic modeling under two views. The idea is to split documents into two views and model the topic correspondence across the two views. We propose two new models that work on a set of document pairs (documents with two views) to discover their corresponding topics. Experimental results show that the proposed approach significantly outperforms strong baselines.
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Acknowledgements
This work was supported in part by a grant from National Science Foundation (NSF) under grant no. IIS-1407927, a NCI grant under grant no. R01CA192240, and a gift from Bosch. The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of the NSF, NCI, or Bosch.
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Fei, G., Chen, Z.(., Mukherjee, A., Liu, B. (2018). Discovering Correspondence of Sentiment Words and Aspects. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9624. Springer, Cham. https://doi.org/10.1007/978-3-319-75487-1_18
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DOI: https://doi.org/10.1007/978-3-319-75487-1_18
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