Getting insights from the voices of customers: Conversation mining at a contact center

Information Sciences

Volumn 179, Issue 11, 2009, Pages 1584-1591

  Takeuchi, Hironori ^a   Subramaniam, L Venkata ^a   Nasukawa, Tetsuya ^a   Roy, Shourya ^a  

^a IBM RESEARCH   (United States)

Author keywords

Contact center; Conversation mining; Transcribed data

Indexed keywords

TRANSCRIPTION;

BUSINESS OUTCOMES; CAR RENTALS; CHANCE DISCOVERIES; CONTACT CENTER; CONVERSATION MINING; KEY ENTITIES; OPERATIONAL EFFICIENCIES; PRODUCT AND SERVICES; REAL-LIFE DATUM; SERVICE CENTERS; TEXT MININGS; TEXTUAL DATUM; TRANSCRIBED DATA;

MINING;

EID: 62249188076     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2008.11.026     Document Type: Article

References (22)

1. Chen M.-C., Chen L.-S., Hsu C.-C., and Zeng W.-R. An information granulation based data mining approach for classifying imbalanced data. Information Sciences (2008) 3214-3227
2. Chen Y., Tsai F.S., and Chan K.L. Machine learning techniques for business blog search and mining. Expert Systems with Applications (2008) 581-590
3. Douglas S., Agarwal D., Alonso T., Bell R.M., Gilbert M., Swayne D.F., and Volinsky C. Mining customer care dialogs for "daily news". IEEE Transaction on Speech and Audio Processing 13 5 (2005) 652-660
4. P. Haffner, G. Tur, J.H. Wright, Optimizing svms for complex call classification, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003, pp. 632-635.
5. H.W. Hastie, R. Prasad, M.A. Walker, What's the trouble: automatically identifying problematic dialogues in darpa communicator dialogue systems, in: Proceedings of the 40th Annual Meeting of the ACL, 2002, pp. 384-391.
6. T. Hisamitsu, Y. Niwa, 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), 2002, pp. 1-7.
7. Hu H.-L., and Chen Y.-L. Mining typical patterns from databases. Information Sciences (2008) 3683-3696
8. T. Joachims, Text categorization with support vector machines: learning with many relevant features, in: Proceedings of the 10th European Conference on Machine Learning (ECML), 1998, pp. 137-142.
9. T. Joachims, Optimizing search engines using clickthrough data, in: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), 2002, pp. 133-142.
10. Kuo H.-K.J., and Lee C.-H. Discriminative training of natural language call routers. IEEE Transaction on Speech and Audio Processing 11 1 (2003) 24-35
11. G. Mishne, D. Carmel, R. Hoory, A. Roytman, A. Soffer, Automatic analysis of call-center conversations, in: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), 2005, pp. 453-459.
12. Nasukawa T., and Nagano T. Text analysis and knowledge mining system. IBM Systems Journal (2001) 967-984
13. Padmanabhan D., and Kummamuru K. Mining conversational text for procedures with applications in contact centers. International Journal on Document Analysis and Recognition (2007) 227-238
14. S. Roy, L.V. Subramaniam, 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), 2006, pp. 737-744.
15. Simons T. Speech patterns and the concept of utility in cognitive maps: the case of integrative bargaining. The Academy of Management Journal 36 1 (1993) 139-156
16. M. Sokolova, V. Nastase, S. Szpakowicz, The telling tail: signals of success in electronic negotiation texts, in: Proceedings of the Third International Joint Conference on Natural Language Processing (IJCNLP 2008), 2008, pp. 257-264.
17. H. Takeuchi, L.V. Subramaniam, T. Nasukawa, S. Roy, 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), 2007, pp. 458-467.
18. M. Tang, B. Pellom, K. Hacioglu, Call-type classification and unsupervised training for the call center domain, in: Proceesings of IEEE Workshop on Automatic Speech Recognition and Understanding, 2003, pp. 204-208.
19. Walker M.A., Langkilde-Geary I., Hastie H.W., Wright J., and Gorin A. Automatically training a problematic dialogue predictor for a spoken dialogue system. Journal of Artificial Intelligence Research 16 (2002) 293-319
20. Y. Yang, J.O. Pedersen, A comparative study on feature selection in text categorization, in: Proceedings of the 14th International Conference on Machine Learning (ICML), 1997, pp. 412-420.
21. Y. Yang, X. Liu, A re-examination of text categorization methods, in: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 42-49.
22. G. Zweig, O. Shiohan, G. Saon, B. Ramabhadran, D. Povey, L. Mangu, B. Kingsbury, Automatic analysis of call-center conversations, in: Proceedings of IEEE Internatinal Conference of Acoustics, Speech and Signal Processing (ICASSP), 2006, pp. 589-592.