Open AccessA customer feedback sentiment dictionary: Towards automatic assessment of online reviews
Author(s) -
Laurens Tetzlaff,
Katrin Rulle,
Gero Szepannek,
Werner Gronau
Publication year - 2019
Publication title -
european journal of tourism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.467
H-Index - 16
eISSN - 1314-0817
pISSN - 1994-7658
DOI - 10.54055/ejtr.v23i.387
Subject(s) - computer science , context (archaeology) , sentiment analysis , lasso (programming language) , relevance (law) , artificial intelligence , hospitality , set (abstract data type) , natural language processing , hospitality industry , machine learning , data science , information retrieval , tourism , world wide web , paleontology , political science , law , biology , programming language
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.