The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Deep learning for sentiment analysis: A survey
Open accessAbstract
Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with the success of deep learning in many application domains, deep learning is also used in sentiment analysis in recent years. This paper gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis. This article is categorized under:Fundamental Concepts of Data and Knowledge > Data Concepts Algorithmic Development > Text Mining
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Citations
Cites
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Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Attention-based LSTM for Aspect-level Sentiment Classification
Wang, Y., Huang, M., zhu, X., & Zhao, L. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1058 · Cited by 2,450
Tensor Fusion Network for Multimodal Sentiment Analysis
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Cited by
A Review of Machine Learning and Deep Learning Applications
Shinde, P. P., & Shah, S. (2018) · 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) · doi:10.1109/iccubea.2018.8697857 · Cited by 773
Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
Chan, J. Y.-L., Leow, S. M. H., Bea, K. T., Cheng, W. K., Phoong, S. W., Hong, Z.-W., & Chen, Y.-L. (2022) · Mathematics · doi:10.3390/math10081283 · Cited by 617
Sentiment Analysis and Opinion Mining
Zhang, L., & Liu, B. (2023) · Encyclopedia of Machine Learning and Data Science · doi:10.1007/978-1-4899-7502-7_907-2 · Cited by 518
Self-citationJang, B., Kim, M., Harerimana, G., Kang, S.-U., & Kim, J. W. (2020) · Applied Sciences · doi:10.3390/app10175841 · Cited by 442
“Natural-language-based sentiment classification has a wide range of applications, such as movie review classification, subjective and objective sentence classification, and text classification technology [1].”
BackgroundSystematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Metadata
- Authors
- Zhang, L., Wang, S., & Liu, B.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2018-03-30
- Volume
- 8
- Issue
- 4
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2964236337
- Cited by
- 1,928
- References
- 13
- FWCI
- 104.96
- Type
- journal article
- Language
- en
- Open access
- bronze
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