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Sentiment Analysis in Customer Relationship Management

Sentiment Analysis in Customer Relationship Management
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Author(s): Souvik Banerjee (Management Development Institute, Murshidabad, India), Abhijit Pandit (Management Development Institute, Murshidabad, India), Timilehin Olasoji Olubiyi (West Midlands Open University, Nigeria)and Raghavendra Prasanna Kumar (School of Business and Management, Christ University, India)
Copyright: 2025
Pages: 18
Source title: Demystifying Emotion AI, Robotics AI, and Sentiment Analysis in Customer Relationship Management
Source Author(s)/Editor(s): Fazla Rabby (Stanford Institute of Management and Technology, Australia), Nasim Ahmed (The University of Sydney, Australia), Amandeep Sehmi (Canterbury Institute of Management, Australia), Rohit Bansal (Rockford College, Sydney, Australia)and Nishita Pruthi (Asian School of Business, India)
DOI: 10.4018/979-8-3373-1867-7.ch008

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Abstract

Modern networking conversations generate annotated metadata, necessitating a method for synthesizing insights from statistics. Emotion detection is crucial for practical conversations, distinguishing joy, grief, and wrath. Corpora are becoming the standard for human-machine interaction, aiming to make interactions feel natural and real. A paradigm that identifies debates and customer views can provide a human touch to these interactions. Researchers developed a machine learning framework for assessing emotions in English phrases, utilizing LSTM (Long Short Term Memory) perspective and real-time emotion recognition in idiomatic speech. Emotion recognition rule (ERR) is created using ontologies like Word Net and Concept Net, Naive Bayes, and Random Forest. Real-time analysis of written words and facial expressions significantly outperforms current algorithms and commandment classifiers in identifying emotional states.

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