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Application of Deep Learning Approaches for Sentiment Analysis

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Deep Learning-Based Approaches for Sentiment Analysis

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Social media platforms, forums, blogs, and opinion sites generate vast amount of data. Such data in the form of opinions, emotions, and views about services, politics, and products are characterized by unstructured format. End users, business industries, and politicians are highly influenced by sentiments of the people expressed on social media platforms. Therefore, extracting, analyzing, summarizing, and predicting the sentiments from large unstructured data needs automated sentiment analysis. Sentiment analysis is an automated process of extracting the opinionated from data and classifying the sentiments as positive, negative, and neutral. Lack of enough labeled data for sentiment analysis is one of the crucial challenges in natural language processing. Deep learning has emanated as one of the highly sought-after solutions to address this challenge due to automated and hierarchical learning capability inherently supported by deep learning models. Considering the application of deep learning approaches for sentiment analysis, this chapter aims to put forth taxonomy of traits to be considered for deep learning-based sentiment analysis and demystify the role of deep learning approaches for sentiment analysis.

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Authors and Affiliations

  1. School of Computer Engineering, Kalinga Institute of Industrial Technology University (KIIT), Bhubaneswar, 751024, India

    Ajeet Ram Pathak, Manjusha Pandey & Siddharth Rautaray

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Kota, 302017, India

    Basant Agarwal

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  1. Ajeet Ram Pathak
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  2. Basant Agarwal
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  3. Manjusha Pandey
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  4. Siddharth Rautaray
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  1. Indian Institute of Information Technology Kota (IIIT-Kota), Jaipur, Rajasthan, India

    Basant Agarwal

  2. Faculty of Science and Engineering, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD, Australia

    Richi Nayak

  3. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Namita Mittal

  4. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

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Pathak, A.R., Agarwal, B., Pandey, M., Rautaray, S. (2020). Application of Deep Learning Approaches for Sentiment Analysis. In: Agarwal, B., Nayak, R., Mittal, N., Patnaik, S. (eds) Deep Learning-Based Approaches for Sentiment Analysis. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-1216-2_1

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