Sentiment analysis in customer relationship management
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. 2025, IGI Global Scientific Publishing. All rights reserved.
Banerjee, Souvik; Pandit, Abhijit; Olubiyi, Timilehin Olasoji; Kumar, Raghavendra Prasanna
Demystifying Emotion AI, Robotics AI, and Sentiment Analysis in Customer Relationship Management;pp.161-177
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.4018/979-8-3373-1867-7.ch008 https://www.scopus.com/pages/publications/105008364853?origin=resultslist
Banerjee S., Management Development Institute, Murshidabad, India; Pandit A., Management Development Institute, Murshidabad, India; Olubiyi T.O., West Midlands Open University, Nigeria; Kumar R.P., School of Business and Management, Christ University, India