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Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures

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Abstract

Microblogging websites such as twitter and Sina Weibo have attracted many users to share their experiences and express their opinions on a variety of topics, making them ideal platforms on which to conduct electronic opinion polls on products, services and public figures. However, conventional sentiment analysis methods for microblogging messages may not meet the demands of opinion polls for public figures. Therefore, in this study, we focus mainly on the problem of sentiment analysis for opinion polling on Chinese public figures. We propose a sentiment parsing-based architecture, which represents and labels opinion targets and their corresponding sentiments jointly to avoid the mismatching of them, for opinion poll of public figures using microblogs. Furthermore, we formulate sentiment parsing of microblogging sentences as a sequence labeling problem and adapt different Recurrent Neural Network (RNN) models to train and infer the model. Our experimental results demonstrate that the proposed sentiment parsing-based methods achieve better performance than conventional sentiment score-based methods in opinion polling on public figures using microblogs.

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Notes

  1. http://ictclas.nlpir.org/downloads.

  2. https://github.com/fchollet/keras.

  3. http://radimrehurek.com/gensim/.

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Acknowledgments

The research is supported by National Natural Science Foundation of China (No.71331008).

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Authors and Affiliations

  1. College of Information Systems and Management, National University of Defense Technology, Changsha, People’s Republic of China

    Jiajun Cheng, Xin Zhang, Pei Li, Sheng Zhang, Zhaoyun Ding & Hui Wang

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  1. Jiajun Cheng
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  2. Xin Zhang
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  3. Pei Li
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  4. Sheng Zhang
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  5. Zhaoyun Ding
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Correspondence to Jiajun Cheng.

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Cheng, J., Zhang, X., Li, P. et al. Exploring sentiment parsing of microblogging texts for opinion polling on chinese public figures. Appl Intell 45, 429–442 (2016). https://doi.org/10.1007/s10489-016-0768-0

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