A customer feedback sentiment dictionary: Towards automatic assessment of online reviews

Authors

  • Laurens Tetzlaff University Stralsund, Germany, Stralsund University of Applied Sciences, Zur Schwedenschanze 15 18435 Stralsund Germany, E-mail: laurens.m.tetzlaff@fh-stralsund.de. Tel: +49 171 555 333 2
  • Katrin Rulle University Stralsund, Germany, E-mail: katrin.rulle@fh-stralsund.de
  • Gero Szepannek University Stralsund, Germany, E-mail: gero.szepannek@hochschule-stralsund.de
  • Werner Gronau University Stralsund, Germany, E-mail: werner.gronau@hochschule-stralsund.de

Keywords:

Customer Reviews, Dictionary Creation, Hotel Online Reviews, LASSO, Sentiment Analysis, Text Mining

Abstract

This paper aims at creating a tool to automatically extract online customer reviews of hospitality businesses and to assign a reliable score to them, based on a specifically created sentiment dictionary for this purpose by means of a statistical learning method. The effect of the amount of available training data and their resulting dictionaries is investigated. As such, a practical approach for applying LASSO regression in the context of online hospitality reviews is presented resulting in a sentiment dictionary of 778 terms with their associated weights trained on 20 000 reviews. It is shown that the created dictionary is able to accurately predict online review scores set by consumers, therefore highlighting the practical relevance of the proposed approach.

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Published

2019-10-01

How to Cite

Tetzlaff, L., Rulle, K., Szepannek, G., & Gronau, W. (2019). A customer feedback sentiment dictionary: Towards automatic assessment of online reviews. European Journal of Tourism Research, 23, 28 - 39. Retrieved from https://ejtr.vumk.eu/index.php/about/article/view/387

Issue

Section

Special issue section