Abstract
In order to classify the speech and information published on the social network platform, this paper proposes an emotion classification method, based on text word vector and deep learning. According to the characteristic of weibo short text itself, the corpus is preprocessed. This paper uses word2vec to obtain the text vector of weibo short text, and classifies emotion through the classification model which is based on XGBoost. The experimental results for NLPCC corpus show that this method achieves a good emotion classification results, and can effectively improve the accuracy of sentiment classification.
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Acknowledgement
Work described in this paper was funded by the National Natural Science Foundation of China under Grant No. 71671093. The authors would like to thank for the help of the college innovation team and other researchers at Nanjing University of Posts and Telecommunications.
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Huang, W., Yao, X., Wang, Q. (2019). Research on Weibo Emotion Classification Based on Context. In: Tang, Y., Zu, Q., RodrÃguez GarcÃa, J. (eds) Human Centered Computing. HCC 2018. Lecture Notes in Computer Science(), vol 11354. Springer, Cham. https://doi.org/10.1007/978-3-030-15127-0_23
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DOI: https://doi.org/10.1007/978-3-030-15127-0_23
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