Identifying user experiences for decision-making in service science

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Abstract

The purpose of this paper is to propose a method based on text mining techniques that employs user comments on the Web to clarify users’ experiences of a service. The method classifies user experiences into the categories of instrumental qualities, non-instrumental qualities, and emotional reactions. The Text-Miner Software Kit (TMSK) and Text Rule Induction Kit (RIKTEXT) were used to obtain the classification rule sets. A case study on the tourist domain was conducted to assess the rules’ precision in classifying sentences into the three user experience categories. One thousand hotel reviews from the website www.tripadvisor.com were used in the evaluation process. Two datasets were built: one for training and one for testing. For validation, 100 new opinions were used to assess the rules’ precision. The results are considered good because there was only a 10% error in automatic classification versus manual classification. Based on this result, we argue that the automatic identification of information about user experience of a service can be done accurately using text mining techniques, such as those presented in this work.

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APA

Aciar, S., Coto, M., & Aciar, G. (2020). Identifying user experiences for decision-making in service science. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12195 LNCS, pp. 147–157). Springer. https://doi.org/10.1007/978-3-030-49576-3_10

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