Abstract
Analyzing failure trends and establishing effective coping processes for complex problems in advance is essential in telecommunication services. We propose a method for semantically analyzing and classifying customer enquiries efficiently and precisely. Our method can also construct semantic content efficiently by extracting related terms through analysis and classification. This method is based on a dependency parsing and co-occurrence technique to enable classification of a large amount of unstructured data into patterns because customer enquiries are generally stored as unstructured textual data.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ohsumi, N.: Mining of textual data. Recent trend and its direction, http://wordminer.comquest.co.jp/wmtips/pdf/20060910_1.pdf
Sato, S., Fukuda, K., Sugawara, S., Kurihara, S.: On the relationship between word bursts in document streams and clusters in lexical co-occurrence networks. IPSJ 48-SIG14, 69–81 (2007)
Sullivan, D.: Document Warehousing and Text Mining. John Wiley, Chichester (2001)
Toda, H., Kataoka, R., Kitagawa, H.: Clustering news articles using named entities. IPSJ SIG Technical Report, 2005-DBS-137, pp.175–181 (2005)
Takahashi, S., Takahashi, S., Yasuda, N., Takahata, N., Ishikawa, T.: A Meaningful Keywords Extracting system based on A Sentence-Semantic Analysis Method. In: IPSJ, AI TR, vol. 90-8, pp. 65–72 (1992)
Akiba, Y., Tanaka, T., Suyama, T., Nagata, M.: Grading Examninee’s Answer Sentences by Verifying Syntactic and Semantic Compatibility. In: IPSJ, SIG TR, 2006-NL-174(b), pp. 31–35 (2006)
Burnstein, J., Kukich, K., Wolff, S., Lu, C., Chodorow, M., Braden-Harder, L., Harris, M.D.: Automated scoring using a hybrid feature identification technique. In: Proc. of Thirty-Sixth Annual Meeting of the Association for Computational Linguistics and Seventeenth International Conference on Computational Linguistics, ACL-COLING 1998, pp. 206–210 (1998)
Taira, H., Mukouchi, T., Haruno, M.: Text Categorization Using Support Vector Machine. In: IPSJ, NL TR, 128-24, pp.173–180 (1998)
Sato, I., Nakagawa, H.: Mining Semi-structure for Text with Dependency Structure. In: IPSJ, SIG TR, 2006-DBS-140(II), pp. 207–214 (2006)
Agrawal, R., Srikaut, R.: Mining Sequential Patterns. In: Proc. of ICDE 1995, pp. 3–14. IEEE Computer Society Press, Los Alamitos (1995)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
Kawatani, T.: Document Clustering via Commonality Analysis of Multiple Documents. In: IPSJ, NL TR, 154-14, pp. 93–100 (2003)
Iwashita, M., Nishimatsu, K., Shimogawa, S.: Semantic analysis method for unstructured data in telecom services. In: Proc. of 2008 IEEE International Conference on Data Mining Workshops, pp. 789–795 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iwashita, M., Shimogawa, S., Nishimatsu, K. (2009). Text Mining for Customer Enquiries in Telecommunication Services. In: Velásquez, J.D., RÃos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5712. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04592-9_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-04592-9_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04591-2
Online ISBN: 978-3-642-04592-9
eBook Packages: Computer ScienceComputer Science (R0)