Abstract
A sentiment dictionary is a valuable resource in sentiment analysis research. Previous work has propagated sentiment values from existing dictionaries via semantic networks to build wide-coverage dictionaries efficiently. Unfortunately, this blind propagation method tends to incorrectly estimate sentiment values the further along the chain it goes from the seed word because it does not consider word senses in context. In this work, we propose a context-aware propagation method on Chinese ConceptNet to help resolve this issue. In our approach, we represent contexts using LDA topic modeling by generating a topic for each context. We can then assign concepts different sentiment values for different topics when propagating sentiments on Chinese ConceptNet. Our experiments on both microblog posts and drama dialogue subtitles show that our context-aware approach improves the accuracy of sentiment polarity prediction.
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Acknowledgments
This study was funded by the Ministry of Science and Technology, National Taiwan University, and Intel Corporation under Grants MOST 102-2627-E-002-001, 103-3113-E-002-008-, 103-2911-I-002-001, 104-2119-M-008-025, 104-2221-E-008 -034 -MY3, NTU-ICRP-104R7501, NTU-ICRP-104R7501-1, and 104R890861.
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Po-Hao Chou declares that he has no conflict of interest. Richard Tzong-Han Tsai declares that he has no conflict of interest. Jane Yung-jen Hsu declares that she has no conflict of interest.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.
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Communicated by C.-H. Chen.
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Chou, PH., Tsai, R.TH. & Hsu, J.Yj. Context-aware sentiment propagation using LDA topic modeling on Chinese ConceptNet. Soft Comput 21, 2911–2921 (2017). https://doi.org/10.1007/s00500-016-2273-0
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DOI: https://doi.org/10.1007/s00500-016-2273-0