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Text Mining of Business-Oriented Conversations at a Call Center

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Data Mining for Service

Part of the book series: Studies in Big Data ((SBD,volume 3))

Abstract

Recently textual record of the telephone conversation at the contact center can be transcribed by the automatic speech recognition technology. In this research, we extend the text mining system for the call summary records and construct a conversation mining system for the business-oriented conversation at the contact center. To acquire useful business insights from the conversation data through the text mining system, it is critical to identify appropriate textual segments and expressions as viewpoints to focus on. In the analysis of call summary data using a text mining system, some experts defined the viewpoints for the analysis by looking some sample records and prepared the dictionaries based on frequent keywords in the sample dataset. It is however difficult to identify such viewpoints manually in advance because the target data is consists of complete transcripts that are often lengthy and redundant. In this research, we define the model of the business-oriented conversations and propose a mining method to identify segments that make impact on the outcome of the conversation and extract useful expressions in each identified segments. In the experiment, we process the real datasets from a car rental service center and construct a mining system. Through the system, we show the effectiveness of the method based on the defined conversation model.

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References

  1. Tang, M., Pellom, B., Hacioglu, K.: Call-type classification and unsupervised training for the call center domain. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 204–208. (2003)

    Google Scholar 

  2. Zweig, G., Shiohan, O., Saon, G., Ramabhadran, B., Povey, D., Mangu, L., Kingsbury, B.: Automatic analysis of call-center conversations. In: Proceedings of IEEE Internatinal Conference of Acoustics, Speech and Signal Processing (ICASSP), pp. 589–592. (2006)

    Google Scholar 

  3. Haffner, P., Tur, G., Wright, J.H.: Optimizing svms for complex call classification. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 632–635. (2003)

    Google Scholar 

  4. Kuo, H.K.J., Lee, C.H.: Discriminative training of natural language call routers. IEEE Trans. Speech and Audio Process. 11(1), 24–35 (2003)

    Article  Google Scholar 

  5. Douglas, S., Agarwal, D., Alonso, T., Bell, R.M., Gilbert, M., Swayne, D.F., Volinsky, C.: Mining customer care dialogs for “daily news”. IEEE Trans. Speech and Audio Process. 13(5), 652–660 (2005)

    Article  Google Scholar 

  6. Mishne, G., Carmel, D., Hoory, R., Roytman, A., Soffer, A.: Automatic analysis of call-center conversations. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp. 453–459. (2005)

    Google Scholar 

  7. Roy, S., Subramaniam, L.V.: Automatic generation of domain models for call centers from noisy transcriptions. In: Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL (COLING/ACL), pp. 737–744. (2006)

    Google Scholar 

  8. Hastie, H.W., Prasad, R., Walker, M.A.: What’s the trouble: automatically identifying problematic dialogues in darpa communicator dialogue systems. In: Proceedings of the 40th Annual Meeting of the ACL, pp. 384–391. (2002)

    Google Scholar 

  9. Walker, M.A., Langkilde-Geary, I., Hastie, H.W., Wright, J., Gorin, A.: Automatically training a problematic dialogue predictor for a spoken dialogue system. J. Artif. Intell. Res. 16, 319–393 (2002)

    Google Scholar 

  10. Hu, H.L., Chen, Y.L.: Mining typical patterns from databases. Inform. Sci. 178(19), 3683–3696 (2008)

    Google Scholar 

  11. Chen, M.C., Chen, L.S., Hsu, C.C., Zeng, W.R.: An information granulation based data mining approach for classifying imbalanced data. Inform. Sci. 178, 3214–3227 (2008)

    Google Scholar 

  12. Chen, Y., Tsai, F.S., Chan, K.L.: Machine learning techniques for business blog search and mining. Expert Syst. Appl. 35(3), 581–590 (2008)

    Google Scholar 

  13. Nasukawa, T., Nagano, T.: Text analysis and knowledge mining system. IBM Syst. J. 40(4), 967–984 (2001)

    Google Scholar 

  14. Padmanabhan, D., Kummamuru, K.: Mining conversational text for procedures with applications in contact centers. IJDAR 10(3–4), 227–238 (2007)

    Google Scholar 

  15. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49. (1999)

    Google Scholar 

  16. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning (ECML), pp. 137–142. (1998)

    Google Scholar 

  17. Hisamitsu, T., Niwa, Y.: A measure of term representativeness based on the number of co-occurring sailent words. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING), pp. 1–7. (2002)

    Google Scholar 

  18. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning (ICML), pp. 412–420. (1997)

    Google Scholar 

  19. Joachims, T.: Optimizing search engines using clickthrough data. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), pp. 133–142. (2002)

    Google Scholar 

  20. Padmanabhan, M., Saon, G., Huang, J., Kingsbury, B., Mangu, L.: Automatic speech recognition performance on a voicemail transcription task. IEEE Trans. Speech and Audio Process. 10(7), 433–442 (2002)

    Article  Google Scholar 

  21. Sokolova, M., Nastase, V., Szpakowicz, S.: The telling tail: Signals of success in electronic negotiation texts. In: Proceedings of the 3rd International Joint Conference on Natural Language Processing (IJCNLP 2008), pp. 257–264. (2008)

    Google Scholar 

  22. Simons, T.: Speech patterns and the concept of utility in cognitive maps: the case of integrative bargaining. Acad. Manag. J. 36(1), 139–156 (1993)

    Article  Google Scholar 

  23. Takeuchi, H., Subramaniam, L.V., Nasukawa, T., Roy, S.: Automatic identification of important segments and expressions for mining of bussiness-oriented conversations at contact centers. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 458–467. (2007)

    Google Scholar 

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Correspondence to Hironori Takeuchi .

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Takeuchi, H., Yamaguchi, T. (2014). Text Mining of Business-Oriented Conversations at a Call Center. In: Yada, K. (eds) Data Mining for Service. Studies in Big Data, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45252-9_8

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  • DOI: https://doi.org/10.1007/978-3-642-45252-9_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45251-2

  • Online ISBN: 978-3-642-45252-9

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