Research Article
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Emotion Recognition on Turkish Mobile Operator Turkcell’s Call Center Calls

Year 2024, Volume: 7 Issue: 3, 338 - 345

Yüksel Yurtay , Hüseyin Demirci , Hüseyin Tiryaki , Tekin Altun

https://doi.org/10.35377/saucis...1539409

Abstract

A fundamental component of human intelligence is the capacity for feeling. In addition to being founded on logic and reason, human conduct is also greatly influenced by the emotions that people experience. For the purpose of this study, we classified one thousand real-life call center client voice data in the Turkish language based on the way they expressed their emotions using text emotion detection. We made use of Ekman's emotional labeling and techniques from the field of artificial intelligence, such as deep learning and other similar methods.

Keywords

Emotion recognition Call center Deep learning Artificial intelligence

References

  • R. C. Solomon, "Emotion Definition, Examples, Scope, Structures, & Facts Britannica." Jan. 2024. Accessed: Jan. 17, 2024. [Online]. Available: https://www.britannica.com/science/emotion
  • D. Grandjean, D. Sander, and K. R. Scherer, "Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization," Conscious. Cogn., vol. 17, no. 2, pp. 484–495, Jun. 2008, doi: 10.1016/j.concog.2008.03.019.
  • P. Ekman, "Basic Emotions," in Handbook of Cognition and Emotion, John Wiley & Sons, Ltd, 1999, pp. 45–60. doi: 10.1002/0470013494.ch3.
  • H. Binali and V. Potdar, "Emotion detection state of the art," in Proceedings of the CUBE International Information Technology Conference, in CUBE '12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 501–507. doi: 10.1145/2381716.2381812.
  • X. Jin and Z. Wang, "An Emotion Space Model for Recognition of Emotions in Spoken Chinese," in Affective Computing and Intelligent Interaction, J. Tao, T. Tan, and R. W. Picard, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2005, pp. 397–402. doi: 10.1007/11573548_51.
  • K. R. Scherer, "Appraisal theory," in Handbook of cognition and emotion, Hoboken, NJ, US: John Wiley & Sons Ltd, 1999, pp. 637–663. doi: 10.1002/0470013494.ch30.
  • J. Nicholson, K. Takahashi, and R. Nakatsu, "Emotion Recognition in Speech Using Neural Networks," Neural Comput. Appl., vol. 9, no. 4, pp. 290–296, Dec. 2000, doi: 10.1007/s005210070006.
  • A. Mikuckas, I. Mikuckiene, A. Venckauskas, E. Kazanavicius, R. Lukas, and I. Plauska, “Emotion Recognition in Human Computer Interaction Systems,” Elektron. Ir Elektrotechnika, vol. 20, no. 10, pp. 51–56, Dec. 2014, doi: 10.5755/j01.eee.20.10.8878.
  • W.-J. Yoon, Y.-H. Cho, and K.-S. Park, "A Study of Speech Emotion Recognition and Its Application to Mobile Services," in Ubiquitous Intelligence and Computing, J. Indulska, J. Ma, L. T. Yang, T. Ungerer, and J. Cao, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 758–766. doi: 10.1007/978-3-540-73549-6_74.
  • D. J. France, R. G. Shiavi, S. Silverman, M. Silverman, and M. Wilkes, "Acoustical properties of speech as indicators of depression and suicidal risk," IEEE Trans. Biomed. Eng., vol. 47, no. 7, pp. 829–837, Jul. 2000, doi: 10.1109/10.846676.
  • T. H. Falk and W.-Y. Chan, "Modulation Spectral Features for Robust Far-Field Speaker Identification," IEEE Trans. Audio Speech Lang. Process., vol. 18, no. 1, pp. 90–100, Jan. 2010, doi: 10.1109/TASL.2009.2023679.
  • S. Patil and G. K. Kharate, "A Review on Emotional Speech Recognition: Resources, Features, and Classifiers," in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India: IEEE, Oct. 2020, pp. 669–674. doi: 10.1109/ICCCA49541.2020.9250765.
  • H. Gunes and M. Pantic, "Automatic, Dimensional and Continuous Emotion Recognition," Int. J. Synth. Emot. IJSE, vol. 1, no. 1, pp. 68–99, Jan. 2010, doi: 10.4018/jse.2010101605.
  • S. Y. M. Lee, Y. Chen, and C.-R. Huang, "A Text-driven Rule-based System for Emotion Cause Detection," in Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, D. Inkpen and C. Strapparava, Eds., Los Angeles, CA: Association for Computational Linguistics, Jun. 2010, pp. 45–53. Accessed: Jan. 17, 2024. [Online]. Available: https://aclanthology.org/W10-0206
  • O. Udochukwu and Y. He, "A Rule-Based Approach to Implicit Emotion Detection in Text," in Natural Language Processing and Information Systems, C. Biemann, S. Handschuh, A. Freitas, F. Meziane, and E. Métais, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015, pp. 197–203. doi: 10.1007/978-3-319-19581-0_17.
  • C. O. Alm, D. Roth, and R. Sproat, "Emotions from Text: Machine Learning for Text-based Emotion Prediction," in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, R. Mooney, C. Brew, L.-F. Chien, and K. Kirchhoff, Eds., Vancouver, British Columbia, Canada: Association for Computational Linguistics, Oct. 2005, pp. 579–586. Accessed: Jan. 17, 2024. [Online]. Available: https://aclanthology.org/H05-1073
  • S. Aman and S. Szpakowicz, "Identifying Expressions of Emotion in Text," in Text, Speech and Dialogue, V. Matoušek and P. Mautner, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 196–205. doi: 10.1007/978-3-540-74628-7_27.
  • C. Baziotis et al., "NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning," in Proceedings of the 12th International Workshop on Semantic Evaluation, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds., New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 245–255. doi: 10.18653/v1/S18-1037.
  • A. Ezen-Can and E. F. Can, "RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem," in Proceedings of the 12th International Workshop on Semantic Evaluation, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds., New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 162–166. doi: 10.18653/v1/S18-1023.
  • W. Amelia and N. U. Maulidevi, "Dominant emotion recognition in short story using keyword spotting technique and learning-based method," in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), Aug. 2016, pp. 1–6. doi: 10.1109/ICAICTA.2016.7803131.
  • S. Gievska, K. Koroveshovski, and T. Chavdarova, "A Hybrid Approach for Emotion Detection in Support of Affective Interaction," in 2014 IEEE International Conference on Data Mining Workshop, Dec. 2014, pp. 352–359. doi: 10.1109/ICDMW.2014.130.
  • K. Shrivastava, S. Kumar, and D. K. Jain, "An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network," Multimed. Tools Appl., vol. 78, no. 20, pp. 29607–29639, Oct. 2019, doi: 10.1007/s11042-019-07813-9.
  • P. Rathnayaka et al., "Gated recurrent neural network approach for multilabel emotion detection in microblogs," arXiv.org. Jul. 2019. Accessed: Jan. 20, 2024. [Online]. Available: https://arxiv.org/abs/1907.07653v1
  • A. Seyeditabari, N. Tabari, S. Gholizadeh, and W. Zadrozny, "Emotion detection in text: focusing on latent representation," arXiv.org. Jul. 2019. Accessed: Jan. 20, 2024. [Online]. Available: https://arxiv.org/abs/1907.09369v1
  • S. Ge, T. Qi, C. Wu, and Y. Huang, "Thu_ngn at semeval-2019 task 3: dialog emotion classification using attentional lstm-cnn," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 340–344. doi: 10.18653/v1/S19-2059.
  • L. Ma, L. Zhang, W. Ye, and W. Hu, "PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 287–291. doi: 10.18653/v1/S19-2049.
  • W. Ragheb, J. Azé, S. Bringay, and M. Servajean, "LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 251–255. doi: 10.18653/v1/S19-2042.
  • J. Xiao, "Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 220–224. doi: 10.18653/v1/S19-2036.
  • A. Basile, M. Franco-Salvador, N. Pawar, S. Štajner, M. Chinea Rios, and Y. Benajiba, "SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 330–334. doi: 10.18653/v1/S19-2057.

Year 2024, Volume: 7 Issue: 3, 338 - 345

Yüksel Yurtay , Hüseyin Demirci , Hüseyin Tiryaki , Tekin Altun

https://doi.org/10.35377/saucis...1539409

Abstract

References

  • R. C. Solomon, "Emotion Definition, Examples, Scope, Structures, & Facts Britannica." Jan. 2024. Accessed: Jan. 17, 2024. [Online]. Available: https://www.britannica.com/science/emotion
  • D. Grandjean, D. Sander, and K. R. Scherer, "Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization," Conscious. Cogn., vol. 17, no. 2, pp. 484–495, Jun. 2008, doi: 10.1016/j.concog.2008.03.019.
  • P. Ekman, "Basic Emotions," in Handbook of Cognition and Emotion, John Wiley & Sons, Ltd, 1999, pp. 45–60. doi: 10.1002/0470013494.ch3.
  • H. Binali and V. Potdar, "Emotion detection state of the art," in Proceedings of the CUBE International Information Technology Conference, in CUBE '12. New York, NY, USA: Association for Computing Machinery, Sep. 2012, pp. 501–507. doi: 10.1145/2381716.2381812.
  • X. Jin and Z. Wang, "An Emotion Space Model for Recognition of Emotions in Spoken Chinese," in Affective Computing and Intelligent Interaction, J. Tao, T. Tan, and R. W. Picard, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2005, pp. 397–402. doi: 10.1007/11573548_51.
  • K. R. Scherer, "Appraisal theory," in Handbook of cognition and emotion, Hoboken, NJ, US: John Wiley & Sons Ltd, 1999, pp. 637–663. doi: 10.1002/0470013494.ch30.
  • J. Nicholson, K. Takahashi, and R. Nakatsu, "Emotion Recognition in Speech Using Neural Networks," Neural Comput. Appl., vol. 9, no. 4, pp. 290–296, Dec. 2000, doi: 10.1007/s005210070006.
  • A. Mikuckas, I. Mikuckiene, A. Venckauskas, E. Kazanavicius, R. Lukas, and I. Plauska, “Emotion Recognition in Human Computer Interaction Systems,” Elektron. Ir Elektrotechnika, vol. 20, no. 10, pp. 51–56, Dec. 2014, doi: 10.5755/j01.eee.20.10.8878.
  • W.-J. Yoon, Y.-H. Cho, and K.-S. Park, "A Study of Speech Emotion Recognition and Its Application to Mobile Services," in Ubiquitous Intelligence and Computing, J. Indulska, J. Ma, L. T. Yang, T. Ungerer, and J. Cao, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 758–766. doi: 10.1007/978-3-540-73549-6_74.
  • D. J. France, R. G. Shiavi, S. Silverman, M. Silverman, and M. Wilkes, "Acoustical properties of speech as indicators of depression and suicidal risk," IEEE Trans. Biomed. Eng., vol. 47, no. 7, pp. 829–837, Jul. 2000, doi: 10.1109/10.846676.
  • T. H. Falk and W.-Y. Chan, "Modulation Spectral Features for Robust Far-Field Speaker Identification," IEEE Trans. Audio Speech Lang. Process., vol. 18, no. 1, pp. 90–100, Jan. 2010, doi: 10.1109/TASL.2009.2023679.
  • S. Patil and G. K. Kharate, "A Review on Emotional Speech Recognition: Resources, Features, and Classifiers," in 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India: IEEE, Oct. 2020, pp. 669–674. doi: 10.1109/ICCCA49541.2020.9250765.
  • H. Gunes and M. Pantic, "Automatic, Dimensional and Continuous Emotion Recognition," Int. J. Synth. Emot. IJSE, vol. 1, no. 1, pp. 68–99, Jan. 2010, doi: 10.4018/jse.2010101605.
  • S. Y. M. Lee, Y. Chen, and C.-R. Huang, "A Text-driven Rule-based System for Emotion Cause Detection," in Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, D. Inkpen and C. Strapparava, Eds., Los Angeles, CA: Association for Computational Linguistics, Jun. 2010, pp. 45–53. Accessed: Jan. 17, 2024. [Online]. Available: https://aclanthology.org/W10-0206
  • O. Udochukwu and Y. He, "A Rule-Based Approach to Implicit Emotion Detection in Text," in Natural Language Processing and Information Systems, C. Biemann, S. Handschuh, A. Freitas, F. Meziane, and E. Métais, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015, pp. 197–203. doi: 10.1007/978-3-319-19581-0_17.
  • C. O. Alm, D. Roth, and R. Sproat, "Emotions from Text: Machine Learning for Text-based Emotion Prediction," in Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, R. Mooney, C. Brew, L.-F. Chien, and K. Kirchhoff, Eds., Vancouver, British Columbia, Canada: Association for Computational Linguistics, Oct. 2005, pp. 579–586. Accessed: Jan. 17, 2024. [Online]. Available: https://aclanthology.org/H05-1073
  • S. Aman and S. Szpakowicz, "Identifying Expressions of Emotion in Text," in Text, Speech and Dialogue, V. Matoušek and P. Mautner, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2007, pp. 196–205. doi: 10.1007/978-3-540-74628-7_27.
  • C. Baziotis et al., "NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning," in Proceedings of the 12th International Workshop on Semantic Evaluation, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds., New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 245–255. doi: 10.18653/v1/S18-1037.
  • A. Ezen-Can and E. F. Can, "RNN for Affects at SemEval-2018 Task 1: Formulating Affect Identification as a Binary Classification Problem," in Proceedings of the 12th International Workshop on Semantic Evaluation, M. Apidianaki, S. M. Mohammad, J. May, E. Shutova, S. Bethard, and M. Carpuat, Eds., New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 162–166. doi: 10.18653/v1/S18-1023.
  • W. Amelia and N. U. Maulidevi, "Dominant emotion recognition in short story using keyword spotting technique and learning-based method," in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), Aug. 2016, pp. 1–6. doi: 10.1109/ICAICTA.2016.7803131.
  • S. Gievska, K. Koroveshovski, and T. Chavdarova, "A Hybrid Approach for Emotion Detection in Support of Affective Interaction," in 2014 IEEE International Conference on Data Mining Workshop, Dec. 2014, pp. 352–359. doi: 10.1109/ICDMW.2014.130.
  • K. Shrivastava, S. Kumar, and D. K. Jain, "An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network," Multimed. Tools Appl., vol. 78, no. 20, pp. 29607–29639, Oct. 2019, doi: 10.1007/s11042-019-07813-9.
  • P. Rathnayaka et al., "Gated recurrent neural network approach for multilabel emotion detection in microblogs," arXiv.org. Jul. 2019. Accessed: Jan. 20, 2024. [Online]. Available: https://arxiv.org/abs/1907.07653v1
  • A. Seyeditabari, N. Tabari, S. Gholizadeh, and W. Zadrozny, "Emotion detection in text: focusing on latent representation," arXiv.org. Jul. 2019. Accessed: Jan. 20, 2024. [Online]. Available: https://arxiv.org/abs/1907.09369v1
  • S. Ge, T. Qi, C. Wu, and Y. Huang, "Thu_ngn at semeval-2019 task 3: dialog emotion classification using attentional lstm-cnn," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 340–344. doi: 10.18653/v1/S19-2059.
  • L. Ma, L. Zhang, W. Ye, and W. Hu, "PKUSE at SemEval-2019 Task 3: Emotion Detection with Emotion-Oriented Neural Attention Network," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 287–291. doi: 10.18653/v1/S19-2049.
  • W. Ragheb, J. Azé, S. Bringay, and M. Servajean, "LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 251–255. doi: 10.18653/v1/S19-2042.
  • J. Xiao, "Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 220–224. doi: 10.18653/v1/S19-2036.
  • A. Basile, M. Franco-Salvador, N. Pawar, S. Štajner, M. Chinea Rios, and Y. Benajiba, "SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations," in Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota, USA: Association for Computational Linguistics, 2019, pp. 330–334. doi: 10.18653/v1/S19-2057.
There are 29 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Yüksel Yurtay 0000-0003-1814-3432

Hüseyin Demirci 0000-0002-5784-0786

Hüseyin Tiryaki 0009-0000-5291-0450

Tekin Altun 0009-0000-3956-7942

Early Pub Date October 30, 2024
Publication Date
Submission Date August 27, 2024
Acceptance Date September 30, 2024
Published in Issue Year 2024Volume: 7 Issue: 3

Cite

IEEE Y. Yurtay, H. Demirci, H. Tiryaki, and T. Altun, “Emotion Recognition on Turkish Mobile Operator Turkcell’s Call Center Calls”, SAUCIS, vol. 7, no. 3, pp. 338–345, 2024, doi: 10.35377/saucis...1539409.

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