Adegboyega, Ojo and Rizun, Nina
(2019)
Enabling Deeper Linguistic-Based Text Analytics—Construct Development for the Criticality of Negative Service Experience.
IEEE Access, 7.
pp. 169217-169256.
ISSN 2169-3536
Abstract
Significant progress has been made in linguistic-based text analytics particularly with the
increasing availability of data and deep learning computational models for more accurate opinion analysis
and domain-specific entity recognition. In understanding customer service experience from texts, analysis
of sentiments associated with different stages of the service lifecycle is a useful starting point. However,
when richer insights into issues associated with negative sentiments and experiences are desired to inform
intervention, deeper linguistic analyses such as identifying specific touchpoints and the context of the service
users become important. While research in this direction is beginning to emerge in some domains, we are
yet to see similar efforts in the domain of healthcare. We present in this paper the results from our construct
development effort for quantifying how critical a negative patient experience is using different elements
of the available textual feedback as a key basis for prioritizing interventions by service providers. This
involves the identification of the different dimensions of the construct, associated linguistic markers and
metrics to compute the criticality index. We also present the results of the application of our developed
conceptualization to linguistic-based text analysis of a small dataset of patient experience feedback.
| Item Type: |
Article
|
| Keywords: |
Customer experience; construct development; linguistic analysis; intensity markers;
negative event; magnitude of consequences; |
| Academic Unit: |
Faculty of Social Sciences > School of Business |
| Item ID: |
13803 |
| Identification Number: |
https://doi.org/10.1109/ACCESS.2019.2947593 |
| Depositing User: |
Adegboyega Ojo
|
| Date Deposited: |
13 Jan 2021 10:33 |
| Journal or Publication Title: |
IEEE Access |
| Publisher: |
Institute of Electrical and Electronics Engineers |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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