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Chinese National Conference on Social Media Processing

SMP 2017: Social Media Processing pp 256–267Cite as

Context Enhanced Word Vectors for Sentiment Analysis

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 774))

Abstract

Word vectors have become very important features for sentiment analysis. The aim of this paper is to encode sentimental context into pre-trained word vectors for sentiment analysis. The negation and intensity words in a context, as well as the sentimental words are combined to form context enhanced word vectors. Experiments on the datasets of SemEval show that the method of using intensity words has improved the result comparing with the baseline. The context enhanced words vectors from a distant supervision data can increase the similarity of the same polarity and decrease the similarity of the different polarity.

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Notes

  1. 1.

    http://help.sentiment140.com/for-students/.

  2. 2.

    https://code.google.com/archive/p/word2vec/.

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Acknowledgments

We would like to thank the National Natural Science Foundation of China (Grant No. 61673266) for the financial support of this research.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhe Ye & Fang Li

Authors
  1. Zhe Ye
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  2. Fang Li
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Corresponding author

Correspondence to Zhe Ye .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xueqi Cheng

  2. Beijing Jinri Toutiao Technology Co. Ltd , Beijing, China

    Weiying Ma

  3. Arizona State University , Tempe, Arizona, USA

    Huan Liu

  4. Institute of Computing Technology, Chinese Academy of Sciences , Beijing, China

    Huawei Shen

  5. Renmin University of China , Beijing, China

    Shizheng Feng

  6. Microsoft Asia Research , Beijing, China

    Xing Xie

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© 2017 Springer Nature Singapore Pte Ltd.

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Ye, Z., Li, F. (2017). Context Enhanced Word Vectors for Sentiment Analysis. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_21

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  • DOI: https://doi.org/10.1007/978-981-10-6805-8_21

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6804-1

  • Online ISBN: 978-981-10-6805-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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