Abstract
The traditional studies which have based on machine learning are usually supervised learning for sentiment analysis problem, this is costly time and money to build pre-labeled dataset, not domain adaptation and hard to handle unseen data. In this paper, we have approached semi-supervised learning for Vietnamese sentiment analysis, training data is only one document. Many preprocessing techniques have been performed to clean and normalize data, complemented semantic lexicons such as negation handling, intensification handling, also augmented training data from one-document training. In experiments, we have performed various aspects and obtained competitive results which may motivate next propositions.
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Nguyen-Nhat, DK., Duong, HT. (2019). One-Document Training for Vietnamese Sentiment Analysis. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_21
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DOI: https://doi.org/10.1007/978-3-030-34980-6_21
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