Sentiment analysis in customer relationship management

Title

Sentiment analysis in customer relationship management

Creator

Banerjee, Souvik; Pandit, Abhijit; Olubiyi, Timilehin Olasoji; Kumar, Raghavendra Prasanna

Description

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.

Source

Demystifying Emotion AI, Robotics AI, and Sentiment Analysis in Customer Relationship Management;pp.161-177

Date

01-01-2025

Publisher

IGI Global

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833731869-1; 979-833731867-7;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/979-8-3373-1867-7.ch008

https://www.scopus.com/pages/publications/105008364853?origin=resultslist