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Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Fuzzy Multi-label Classification of Customer Complaint Logs Under Noisy Environment

Authors : Tirthankar Dasgupta, Lipika Dey, Ishan Verma

Published in: Rough Sets

Publisher: Springer International Publishing

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Abstract

Analyzing and understanding customer complaints has become and important issue in almost all enterprises. With respect to this, one of the key factors involve is to automatically identify and analyze the different causes of the complaints. A single complaint may belong to multiple complaint domains with fuzzy associations to each of the different domains. Thus, single label or multi-class classification techniques may not be suitable for classification of such complaint logs. In this paper, we have analyzed and classified customer complaints of some of the leading telecom service providers in India. Accordingly, we have adopted a fuzzy multi-label text classification approach along with different language independent statistical features to address the above mentioned issue. Our evaluation shows combining the features of point-wise mutual information and unigram returns the best possible result.

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Metadata
Title
Fuzzy Multi-label Classification of Customer Complaint Logs Under Noisy Environment
Authors
Tirthankar Dasgupta
Lipika Dey
Ishan Verma
Copyright Year
2016
Book
Rough Sets

Print ISBN: 978-3-319-47159-4

Electronic ISBN: 978-3-319-47160-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47160-0_34

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