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Deep Learning Model for Sentiment Analysis in Multi-lingual Corpus

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10634))

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Abstract

While most text classification studies focus on monolingual documents, in this article, we propose an empirical study of poly-languages text sentiment classification model, based on Convolutional Networks ConvNets. The novel approach consists on feeding the deep neural network with one input text source composed by reviews all written in different languages, without any code-switching indication, or language translation. We construct a multi-lingual opinion corpus combining three languages: English French and Greek all from Restaurants Reviews. Despite the limited contextual information due to relatively compact text content, no prior knowledge is used. The neural networks exploit n-gram level information, and the experimental results achieve high accuracy for sentiment polarity prediction, both positive and negative, which lead us to deduce that ConvNets features extraction is language independent.

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Authors and Affiliations

  1. LIASD, Université Paris 8, Saint-denis, France

    Lisa Medrouk & Anna Pappa

Authors
  1. Lisa Medrouk
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  2. Anna Pappa
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Corresponding author

Correspondence to Anna Pappa .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Medrouk, L., Pappa, A. (2017). Deep Learning Model for Sentiment Analysis in Multi-lingual Corpus. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_22

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  • DOI: https://doi.org/10.1007/978-3-319-70087-8_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70086-1

  • Online ISBN: 978-3-319-70087-8

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