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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Cites
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Attention-based LSTM for Aspect-level Sentiment Classification
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Tensor Fusion Network for Multimodal Sentiment Analysis
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Cited by
A Review of Machine Learning and Deep Learning Applications
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Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review
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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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