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Sentiment analysis using deep learning approaches: an overview

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Abstract

Nowadays, with the increasing number of Web 2.0 tools, users generate huge amounts of data in an enormous and dynamic way. In this regard, the sentiment analysis appeared to be an important tool that allows the automation of getting insight from the user-generated data. Recently, deep learning approaches have been proposed for different sentiment analysis tasks and have achieved state-of-the-art results. Therefore, in order to help researchers to depict quickly the current progress as well as current issues to be addressed, in this paper, we review deep learning approaches that have been applied to various sentiment analysis tasks and their trends of development. This study also provides the performance analysis of different deep learning models on a particular dataset at the end of each sentiment analysis task. Toward the end, the review highlights current issues and hypothesized solutions to be taken into account in future work. Moreover, based on knowledge learned from previous studies, the future work subsection shows the suggestions that can be incorporated into new deep learning models to yield better performance. Suggestions include the use of bidirectional encoder representations from transformers (BERT), sentiment-specific word embedding models, cognition-based attention models, common sense knowledge, reinforcement learning, and generative adversarial networks.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant Nos. 2016YFB0800402, 2016QY01W0202), National Natural Science Foundation of China (Grant Nos. U1836204, 61572221, 61433006, U1401258, 61572222, 61502185), Major Projects of the National Social Science Foundation (Grant No. 16ZDA092), and Guangxi High Level 1043 Innovation Team in Higher Education Institutions Innovation Team of ASEAN Digital Cloud Big Data Security and Mining Technology.

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  1. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Olivier Habimana, Yuhua Li, Ruixuan Li & Xiwu Gu

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, China

    Ge Yu

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  1. Olivier Habimana
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Correspondence to Yuhua Li or Ruixuan Li.

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Habimana, O., Li, Y., Li, R. et al. Sentiment analysis using deep learning approaches: an overview. Sci. China Inf. Sci. 63, 111102 (2020). https://doi.org/10.1007/s11432-018-9941-6

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