Estimating numerical scale ratings from text-based service reviews
ISSN: 1757-5818
Article publication date: 4 June 2020
Issue publication date: 24 September 2020
Abstract
Purpose
This paper develops a generalizable, machine-learning-based method for measuring established marketing constructs using passive analysis of consumer-generated textual data from service reviews. The method is demonstrated using topic and sentiment analysis along dimensions of an existing scale: lodging quality index (LQI).
Design/methodology/approach
The method induces numerical scale ratings from text-based data such as consumer reviews. This is accomplished by automatically developing a dictionary from words within a set of existing scale items, rather a more manual process. This dictionary is used to analyze textual consumer review data, inducing topic and sentiment along various dimensions. Data produced is equivalent with Likert scores.
Findings
Paired t-tests reveal that the text analysis technique the authors develop produces data that is equivalent to Likert data from the same individual. Results from the authors’ second study apply the method to real-world consumer hotel reviews.
Practical implications
Results demonstrate a novel means of using natural language processing in a way to complement or replace traditional survey methods. The approach the authors outline unlocks the ability to rapidly and efficiently analyze text in terms of any existing scale without the need to first manually develop a dictionary.
Originality/value
The technique makes a methodological contribution by outlining a new means of generating scale-equivalent data from text alone. The method has the potential to both unlock entirely new sources of data and potentially change how service satisfaction is assessed and opens the door for analysis of text in terms of a wider range of constructs.
Keywords
Citation
Tsao, H.-Y., Chen, M.-Y., Campbell, C. and Sands, S. (2020), "Estimating numerical scale ratings from text-based service reviews", Journal of Service Management, Vol. 31 No. 2, pp. 187-202. https://doi.org/10.1108/JOSM-06-2019-0167
Publisher
:Emerald Publishing Limited
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