Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Banerjee, Souvik; Pandit, Abhijit; Olubiyi, Timilehin Olasoji; Kumar, Raghavendra Prasanna Title A name given to the resource Sentiment analysis in customer relationship management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Demystifying Emotion AI, Robotics AI, and Sentiment Analysis in Customer Relationship Management;pp.161-177 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-1867-7.ch008" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-1867-7.ch008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105008364853?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008364853?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833731869-1; 979-833731867-7; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online