Abstract
This paper proposes a fast and high performance classifier model for sentiment analysis of textual reviews. The key contribution is three fold. First, a two-level classifier model consists of three base classifiers is proposed, and theory proves that the model could be better than the strongest classifier among the base classifiers in both classification performance and time cost of predict. Second, this paper proposes a lexicon-based classifier as a base classifier using a new part of speech (POS) which is called “weaken words”. Finally, we implemented several two-level classifiers by combining the lexicon-based classifier with several machine learning classifiers. Experiments on Chinese reviews dataset show that the two-level classifier model is effective and efficient.
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Acknowledgements
This work was supported by the National Science Foundation of China (Grant Nos. 61370059, 61772053 and 61232009 (key project)), Beijing Natural Science Foundation (Grant No. 4152030), the fund of the State Key Laboratory of Computer Architecture (CARCH201507), the fund of the State Key Laboratory of Software Development Environment (SKLSDE-2016ZX-15), the State Administration of Science Technology and Industry for National Defense, the major projects of high resolution earth observation system under Grant No. Y20A-E03, and the National Key R&D Program of China under Grant No. 2017YFB0202004.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hao, H. et al. (2018). A Two-Level Classifier Model for Sentiment Analysis. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_64
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