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Agent Productivity Measurement in Call Center Using Machine Learning

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016 (AISI 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 533))

Abstract

We present an application of sentiment analysis using natural language toolkit (NLTK) for measuring customer service representative (CSR) productivity in real estate call centers. The study describes in details the decisions made, step by step, in building an Arabic system for evaluation and measuring. The system includes transcription method, feature extraction, training process and analysis. The results are analyzed subjectively based on the original test set. The corpus consists of 7 h real estate corpus collected from three different call centers located in Egypt. We draw the baseline of productivity measurement in real estate sector.

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Notes

  1. 1.

    http://www.webmetricsguru.com/archives/2010/04/sentiment-analysis-best-done-by-humans.

  2. 2.

    www.qamus.org/transliteration.htm.

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Acknowledgment

Many thanks for Luminous technology center (info@luminous-technologies.com) for the corpus and giving full access to experiment server. Special Thanks for Dr. Kyoko Fukukawa, Bradford University, Bradford, UK and Dr. Yasser Hifny, Helwan University, Cairo, Egypt for their outstanding effort in this paper.

Author information

Authors and Affiliations

  1. Electronic Engineering Department, University of Seville, Seville, Spain

    Abdelrahman Ahmed, Sergio Toral & Khaled Shaalan

  2. School of Informatics, Edinburgh, UK

    Khaled Shaalan

  3. The British University in Dubai, Dubai, UAE

    Khaled Shaalan

Authors
  1. Abdelrahman Ahmed
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  2. Sergio Toral
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  3. Khaled Shaalan
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Corresponding author

Correspondence to Abdelrahman Ahmed .

Editor information

Editors and Affiliations

  1. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Dubai International Academic City, The British University, Dubai, United Arab Emirates

    Khaled Shaalan

  3. CS Dept. Faculty of Computers and Inform, Suez Canal University CS Dept. Faculty of Computers and Inform, Ismailia, Egypt

    Tarek Gaber

  4. Ahmed Orabi Square , Menouf, Egypt

    Ahmad Taher Azar

  5. Faculty of Computer & Information Scienc, Ain Shams University Faculty of Computer & Information Scienc, Cairo, Egypt

    M. F. Tolba

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Ahmed, A., Toral, S., Shaalan, K. (2017). Agent Productivity Measurement in Call Center Using Machine Learning. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-48308-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48307-8

  • Online ISBN: 978-3-319-48308-5

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