Abstract
Sequences play a major role in the extraction of information from data. As an example, in business intelligence, they can be used to track the evolution of customer behaviors over time or to model relevant relationships. In this paper, we focus our attention on the domain of contact centers, where sequential data typically take the form of oral or written interactions, and word sequences often play a major role in text classification, and we investigate the connections between sequential data and text mining techniques. The main contribution of the paper is a new machine learning algorithm, called J48S, that associates semantic knowledge with telephone conversations. The proposed solution is based on the well-known C4.5 decision tree learner, and it is natively able to mix static, that is, numeric or categorical, data and sequential ones, such as texts, for classification purposes. The algorithm, evaluated in a real business setting, is shown to provide competitive classification performances compared with classical approaches, while generating highly interpretable models and effectively reducing the data preparation effort.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
A detailed account of these aspects is the object of a forthcoming work about the whole speech analytics process.
References
Saberi, M., Khadeer Hussain, O., Chang, E.: Past, present and future of contact centers: a literature review. Bus. Process. Manag. J. 23(3), 574–597 (2017)
Cailliau, F., Cavet, A.: Mining automatic speech transcripts for the retrieval of problematic calls. In: Gelbukh, A. (ed.) CICLing 2013. LNCS, vol. 7817, pp. 83–95. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37256-8_8
Pandharipande, M.A., Kopparapu, S.K.: A novel approach to identify problematic call center conversations. In: Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE 2012), pp. 1–5 (2012)
Garnier-Rizet, M., et al.: CallSurf: automatic transcription, indexing and structuration of call center conversational speech for knowledge extraction and query by content. In: Sixth International Conference on Language Resources and Evaluation (LREC 2008), pp. 2623–2628 (2008)
Quinlan, J.R.: Simplifying decision trees. Int. J. Man Mach. Stud. 27(3), 221–234 (1987)
Fournier-Viger, P., Gomariz, A., Šebek, M., Hlosta, M.: VGEN: fast vertical mining of sequential generator patterns. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 476–488. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10160-6_42
Gans, N., Koole, G., Mandelbaum, A.: Telephone call centers: tutorial, review, and research prospects. Manuf. Serv. Oper. Manag. 5(2), 79–141 (2003)
Fan, W., et al.: Direct mining of discriminative and essential frequent patterns via model-based search tree. In: Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), pp. 230–238 (2008)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2016)
Quinlan, J.R.: Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4(1), 77–90 (1996)
Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1), 1–41 (2010)
Fournier-Viger, P., Lin, J.C.W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recognit. 1(1), 54–77 (2017)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Eleventh IEEE International Conference on Data Engineering (ICDE 1995), pp. 3–14 (1995)
Pei, J., et al.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)
Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1), 31–60 (2001)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2002), pp. 429–435 (2002)
Yan, X., Han, J., Afshar, R.: CloSpan: mining closed sequential patterns in large datasets. In: 2003 SIAM International Conference on Data Mining (SIAM 2003), pp. 166–177 (2003)
Wang, J., Han, J.: BIDE: efficient mining of frequent closed sequences. In: Twentieth IEEE International Conference on Data Engineering (ICDE 2004), pp. 79–90 (2004)
Gomariz, A., Campos, M., Marin, R., Goethals, B.: ClaSP: an efficient algorithm for mining frequent closed sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS, vol. 7818, pp. 50–61. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37453-1_5
Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS, vol. 8443, pp. 40–52. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06608-0_4
Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)
Lo, D., Khoo, S.C., Li, J.: Mining and ranking generators of sequential patterns. In: 2008 SIAM International Conference on Data Mining (SIAM 2008), pp. 553–564 (2008)
Duong, H., Truong, T., Le, B.: Efficient algorithms for simultaneously mining concise representations of sequential patterns based on extended pruning conditions. Eng. Appl. Artif. Intell. 67, 197–210 (2018)
Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective classification. In: Twenty-Third IEEE International Conference on Data Engineering (ICDE 2007), pp. 716–725 (2007)
Jun, B.H., Kim, C.S., Song, H.Y., Kim, J.: A new criterion in selection and discretization of attributes for the generation of decision trees. IEEE Trans. Pattern Anal. Mach. Intell. 19(12), 1371–1375 (1997)
Povey, D., et al.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding (ASRU 2011), pp. 1–4 (2011)
Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Brunello, A., Marzano, E., Montanari, A., Sciavicco, G. (2018). J48S: A Sequence Classification Approach to Text Analysis Based on Decision Trees. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2018. Communications in Computer and Information Science, vol 920. Springer, Cham. https://doi.org/10.1007/978-3-319-99972-2_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-99972-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99971-5
Online ISBN: 978-3-319-99972-2
eBook Packages: Computer ScienceComputer Science (R0)