Abstract
Aspect-based sentiment analysis is an emerging trend in the NLP area nowadays. One of the major tasks in this work is to identify corresponding aspects for rating sentiment. Ontology is considered highly useful to cope with this issue, due to its capability of capturing and representing concepts in a certain domain. However, ontology-based sentiment analysis suffers from the difficulty when handling anaphoric coreference of mentioned entities, which commonly occurs in textual documents. This paper addresses this problem by introducing an approach combining coreference resolution with ontology inference. The initial results are quite promising.
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Acknowledgments
This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2016-20-36. We are also grateful to YouNet Media for supporting real datasets for our experiment.
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Le, T.T., Vo, T.H., Mai, D.T., Quan, T.T., Phan, T.T. (2016). Sentiment Analysis Using Anaphoric Coreference Resolution and Ontology Inference. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_26
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