Abstract
In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ranking algorithm for label selection that jointly considers topic-sentence relevance as well as aspect and sentiment co-coverage. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. Our experimental results show that our approach outperforms four strong baselines and demonstrates the effectiveness of our sentence labels in facilitating topic understanding and interpretation.
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Acknowledgments
This work is supported by the awards made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1, EP/P011829/1).
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Barawi, M.H., Lin, C., Siddharthan, A. (2017). Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels. In: Frasincar, F., Ittoo, A., Nguyen, L., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science(), vol 10260. Springer, Cham. https://doi.org/10.1007/978-3-319-59569-6_38
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DOI: https://doi.org/10.1007/978-3-319-59569-6_38
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