Abstract
The microblogging services become increasingly popular for people to exchange their feelings and opinions. Extracting and analyzing the sentiments in microblogs have drawn extensive attentions from both academia researchers and commercial companies. The previous literature usually focused on classifying the microblogs into positive or negative categories. However, people’s sentiments are much more complex, and multiple fine-grained emotions may coexist in just one short microblog text. In this paper, we regard the emotion analysis as a multi-label learning problem and propose a novel calibrated label ranking based framework for detecting the multiple fine-grained emotions in the Chinese microblogs. We combine the learning-based method and lexicon-based method to build unified emotion classifiers, which alleviate the sparsity of the training microblog dataset. Experiment results using NLPCC 2014 evaluation dataset show that our proposed algorithm has achieved the best performance and significantly outperforms other participators’ methods.
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Wang, M., Liu, M., Feng, S., Wang, D., Zhang, Y. (2014). A Novel Calibrated Label Ranking Based Method for Multiple Emotions Detection in Chinese Microblogs. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_22
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DOI: https://doi.org/10.1007/978-3-662-45924-9_22
Publisher Name: Springer, Berlin, Heidelberg
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