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Deep Learning for Sentiment Analysis: An Overview and Perspectives

Deep Learning for Sentiment Analysis: An Overview and Perspectives

Vincent Karas, Björn W. Schuller
Copyright: © 2021 |Pages: 36
ISBN13: 9781799842408|ISBN10: 1799842401|ISBN13 Softcover: 9781799851349|EISBN13: 9781799842415
DOI: 10.4018/978-1-7998-4240-8.ch005
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MLA

Karas, Vincent, and Björn W. Schuller. "Deep Learning for Sentiment Analysis: An Overview and Perspectives." Natural Language Processing for Global and Local Business, edited by Fatih Pinarbasi and M. Nurdan Taskiran, IGI Global, 2021, pp. 97-132. https://doi.org/10.4018/978-1-7998-4240-8.ch005

APA

Karas, V. & Schuller, B. W. (2021). Deep Learning for Sentiment Analysis: An Overview and Perspectives. In F. Pinarbasi & M. Taskiran (Eds.), Natural Language Processing for Global and Local Business (pp. 97-132). IGI Global. https://doi.org/10.4018/978-1-7998-4240-8.ch005

Chicago

Karas, Vincent, and Björn W. Schuller. "Deep Learning for Sentiment Analysis: An Overview and Perspectives." In Natural Language Processing for Global and Local Business, edited by Fatih Pinarbasi and M. Nurdan Taskiran, 97-132. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-4240-8.ch005

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Abstract

Sentiment analysis is an important area of natural language processing that can help inform business decisions by extracting sentiment information from documents. The purpose of this chapter is to introduce the reader to selected concepts and methods of deep learning and show how deep models can be used to increase performance in sentiment analysis. It discusses the latest advances in the field and covers topics including traditional sentiment analysis approaches, the fundamentals of sentence modelling, popular neural network architectures, autoencoders, attention modelling, transformers, data augmentation methods, the benefits of transfer learning, the potential of adversarial networks, and perspectives on explainable AI. The authors' intent is that through this chapter, the reader can gain an understanding of recent developments in this area as well as current trends and potentials for future research.

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