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A Deep Neural Architecture for Sentence-Level Sentiment Classification in Twitter Social Networking

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Computational Linguistics (PACLING 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 781))

Abstract

This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.

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Notes

  1. 1.

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

  2. 2.

    https://nlp.stanford.edu/projects/glove/.

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Acknowledgment

This work was supported by the JSPS KAKENHI Grant number JP15K16048.

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Authors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Huy Nguyen & Minh-Le Nguyen

Authors
  1. Huy Nguyen
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  2. Minh-Le Nguyen
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Corresponding author

Correspondence to Huy Nguyen .

Editor information

Editors and Affiliations

  1. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Kôiti Hasida

  2. Natural Language Processing Lab, University of Computer Studies, Yangon, Yangon, Myanmar

    Win Pa Pa

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Nguyen, H., Nguyen, ML. (2018). A Deep Neural Architecture for Sentence-Level Sentiment Classification in Twitter Social Networking. In: Hasida, K., Pa, W. (eds) Computational Linguistics. PACLING 2017. Communications in Computer and Information Science, vol 781. Springer, Singapore. https://doi.org/10.1007/978-981-10-8438-6_2

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  • DOI: https://doi.org/10.1007/978-981-10-8438-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8437-9

  • Online ISBN: 978-981-10-8438-6

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