Abstract
This paper proposes an improvement to lifelong learning for cross-domain sentiment classification. Lifelong learning is to retain knowledge from past learning tasks to improve the learning task on a new domain. In this paper, we will discuss how bigram and bag-of-bigram features integrated into a lifelong learning system can help improve the performance of sentiment classification on both Vietnamese and English. Also, pre-processing techniques specifically for our cross-domain, Vietnamese dataset will be discussed. Experimental results show that our method achieves improvements over prior systems and its potential for cross-domain sentiment classification.
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This research is supported by research funding from Honors Program, University of Science, Vietnam National University - Ho Chi Minh City.
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Ha, QV., Nguyen-Hoang, BD., Nghiem, MQ. (2016). Lifelong Learning for Cross-Domain Vietnamese Sentiment Classification. In: Nguyen, H., Snasel, V. (eds) Computational Social Networks. CSoNet 2016. Lecture Notes in Computer Science(), vol 9795. Springer, Cham. https://doi.org/10.1007/978-3-319-42345-6_26
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DOI: https://doi.org/10.1007/978-3-319-42345-6_26
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