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Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications.

Mirosław Płaza1, Sławomir Trusz2, Justyna Kęczkowska1

  • 1Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysiąclecia P.P. 7, 25-314 Kielce, Poland.

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Summary

This study introduces a new emotion classification system for contact center AI. It enhances virtual assistants by detecting customer emotions like anger, fear, happiness, and sadness in voice and text, improving intent recognition.

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call/contact centerchatbotemotions recognitionvirtual assistantvoicebot

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Area of Science:

  • Natural Language Processing
  • Affective Computing
  • Artificial Intelligence in Customer Service

Background:

  • Virtual assistants in contact centers are increasingly popular for customer interaction.
  • Accurate customer intent recognition is crucial but often influenced by unaddressed emotions.
  • Existing research lacks specific emotion classifications for contact center applications.

Purpose of the Study:

  • To develop and evaluate an emotion classification system tailored for contact center environments.
  • To identify relevant emotion types (anger, fear, happiness, sadness, neutrality) impacting conversational content.
  • To improve machine detection of affect-tinged conversational content within the contact center industry.

Main Methods:

  • Consideration of both voice and text channels within contact center communications.
  • Development of a novel emotion classification framework for affect detection.
  • Application of machine learning algorithms including Convolutional Neural Network (CNN) and Support Vector Machine (SVM).

Main Results:

  • The proposed emotion classification demonstrated utility across both voice and text channels.
  • For the voice channel, CNN achieved the highest performance with 67.5% accuracy, 80.3% precision, and 74.5% F1-Score.
  • For the text channel, SVM yielded the best results with 65.9% accuracy, 58.5% precision, and 61.7% F1-Score.

Conclusions:

  • The developed emotion classification system is effective for analyzing customer emotions in contact centers.
  • The findings highlight the importance of emotion detection for enhancing virtual assistant capabilities.
  • Specific machine learning models show differential effectiveness for voice versus text-based emotion classification.