Abstract
Sentiment analysis from the text is an exciting and challenging task which can be useful in many applications of exploiting people interests for improving the quality of services. Especially, text collected from social networks, websites or forums is usually represented by spoken language that is unstructured and difficult to handle. In this paper, we present a novel hybrid model that is based on Hierarchical Dirichlet Process (HDP) and adopts a combination of lexicon-based and Support Vector Machine (SVM) methods in the task of topic-based sentiment classification for Vietnamese text. The proposed model has been evaluated on five different topic-datasets, and the experimental results show the efficiency of our proposed model when the average accuracy is nearly 87%. Although this proposed model is initially designed for Vietnamese language, it is applicable and adaptable to other languages.
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We would like to thank TIS Inc. (www.tis.com) for supporting and funding this research.
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Vo, T.H., Nguyen, T.T., Pham, H.A., Le, T.V. (2017). An Efficient Hybrid Model for Vietnamese Sentiment Analysis. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10191. Springer, Cham. https://doi.org/10.1007/978-3-319-54472-4_22
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DOI: https://doi.org/10.1007/978-3-319-54472-4_22
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