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Predictive Analysis of Emotions for Improving Customer Services

Predictive Analysis of Emotions for Improving Customer Services

Vinay Kumar Jain, Shishir Kumar
Copyright: © 2020 |Pages: 10
ISBN13: 9781799809517|ISBN10: 179980951X|EISBN13: 9781799809524
DOI: 10.4018/978-1-7998-0951-7.ch039
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MLA

Jain, Vinay Kumar, and Shishir Kumar. "Predictive Analysis of Emotions for Improving Customer Services." Natural Language Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 808-817. https://doi.org/10.4018/978-1-7998-0951-7.ch039

APA

Jain, V. K. & Kumar, S. (2020). Predictive Analysis of Emotions for Improving Customer Services. In I. Management Association (Ed.), Natural Language Processing: Concepts, Methodologies, Tools, and Applications (pp. 808-817). IGI Global. https://doi.org/10.4018/978-1-7998-0951-7.ch039

Chicago

Jain, Vinay Kumar, and Shishir Kumar. "Predictive Analysis of Emotions for Improving Customer Services." In Natural Language Processing: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 808-817. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-0951-7.ch039

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Abstract

Human emotions plays an important role in everyday communication. Emotions are formed by the combination of cues such as relative actions, facial expressions, and gestures and reactions. Emotions are also present in written texts like in social media, chats, customer reviews. By getting inspired by works done in the domain of sentiment analysis, this chapter explores advances to automatic detection of emotions in text which help in Improving Customer Services. This chapter presents a framework for automatic detection of emotions in customer reviews based on different emotions theories in the fields of psychology and linguistics. This framework uses advanced Machine Learning (ML) techniques with Natural Language Processing (NLP) methods for better understanding of emotion detection and recognition in customer reviews. The text under study comprises data collected from leading Indian e-commerce portals like Flipkart, Snapdeal and Amazon, which contains text rich in emotions. The advantages and application based emotion detection framework has been incorporated with suitable examples.

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