Yuliya Kolomoyets
Short Bio
Awards
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2019 : ENTER 2019 PhD Workshop Best Presentation award. Runner up ()
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2018 : IFITT Doctoral Summer School 2019. Best Paper Award. Runner up ()
Research Output
- 2019
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"A Text Mining Approach to Measuring and Predicting Perceived Service Quality from Online Chatter"2019
Author(s): Yuliya Kolomoyets, Astrid Dickinger
Publication date: 2019
- 2018
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"Exploring consumer's perception of service quality through online reviews: Text mining approach"2018 Pages: 385
Online reviews are an important source of consumer opinions about the service experiences across industries. Growing number of research investigate the potential of online reviews to explain consumer behaviour and to predict business performance, while only few engage into the examination of the textual content of the reviews. Large volume and unstructured format of the textual data make it unamenable to analysis with traditional methods like survey. To fill the emerging gap, this study will rely on automated text mining methodology to explore how consumers reflect on the perceived service quality in the online reviews. Specifically, sentiment analysis, text classification and regression modelling will be applied to investigate the dimensions of perceived service quality together with their contribution to the overall rating of service experience (e.g. star rating). The results are expected to extend the understanding of perceived service quality; to illustrate the value of the automated text mining methods for maximising the usefulness of online reviews and helping businesses reach their strategic goals.
Author(s): Yuliya Kolomoyets
Publication date: 2018
Pages: 385
-
"A Text Mining Approach to Measuring & Predicting Perceived Service Quality from Online Chatter"2018
Online reviews have been studied for their potential to explain consumer behaviour and to predict business performance. However, large volume and unstructured format of the textual part of the reviews make them unamenable to analysis with traditional methods like survey. By employing the automated text mining methodology this study explores how consumers reflect on the perceived service quality in the online reviews. Specifically, sentiment analysis, text classification and predictive modelling will be applied to investigate the dimensions of perceived service quality together with their contribution to the overall rating of service experience. The results are expected to extend the understanding of perceived service quality; to illustrate the value of the automated text mining methods for maximising the usefulness of online reviews and helping businesses reach their strategic goals.
Author(s): Yuliya Kolomoyets
Publication date: 2018
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