Mining customer reviews to evaluate the contact centre agent performance using custom kernel functions

A. Santhosh Kumar, Punniyamoorthy Murugesan and Ernest Johnson

International Journal of Enterprise Network Management, 2024, vol. 15, issue 3, 245-260

Abstract: In today's digital world, the exponential growth of unstructured text data necessitates businesses to rethink their organisational strategies based on the insights extracted from data using text or opinion mining. To extract opinions from text documents, various machine learning algorithms are utilised, with support vector machine (SVM) being a popular one due to its ability to efficiently classify nonlinear data using the Kernel trick (Kernel function). This function implicitly transforms the input to a higher dimensional vector space, making it easier to classify data linearly. In our study, we have applied the dissimilarity kernel function, which is suitable for sparse data. We evaluated the performance of the new kernel function in classifying opinions from customer feedback in the business to consumer (B2C) contact centre industry and ranked contact centre agents based on the customer feedback data.

Keywords: opinion mining; Jaccard dissimilarity kernel; custom kernel functions; contact centre agent performance. (search for similar items in EconPapers)
Date: 2024
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