Deep learning for sentiment analysis: An overview and perspectives

Vincent Karas, Björn W. Schuller

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

10 Scopus citations

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.

Original languageEnglish
Title of host publicationNatural Language Processing for Global and Local Business
PublisherIGI Global
Pages97-132
Number of pages36
ISBN (Electronic)9781799842415
ISBN (Print)9781799842408
DOIs
StatePublished - 31 Jul 2020
Externally publishedYes

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