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Exploring the Impact of Negative Words Used in Online Feedback in Hotel Industry: A Sentiment Analysis, N-gram, and Text Network Analysis Approach

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  • Daniel Dan

    (Modul University, Vienna, Austria)

Abstract

This study examines the words and situations that trigger and those that do not trigger a hotel response when customers post negative online feedback. The research explores, through sentiment analysis, bigrams, trigrams, and word networking, the valence of online reviews of fi ve important hotels in Las Vegas. Only the feedback that has been categorized as negative by the algorithm is selected. In correspondence to this feedback, the existence of answers from the hotels is checked together with the response style. While the negative valence of the feedback can represent a mixture of subjective and objective emotions, there are common features present in their expression. On the responses side from the hotel, not all the reviews receive attention. As such, the negative feedback words are extracted and separated into those that belong to reviews that obtain a response and those that do not. The replies are standardised by following an established pattern. This paper aims to contribute to a prominent issue in tourism that is little tackled: responses to feedback. The fi ndings may help the hotels’ management explore diff erent paths to improve their services and responses alike. Behavioural marketing researchers might want to use these results to confi rm the existence of such patterns in diff erent datasets or situations.

Suggested Citation

  • Daniel Dan, 2023. "Exploring the Impact of Negative Words Used in Online Feedback in Hotel Industry: A Sentiment Analysis, N-gram, and Text Network Analysis Approach," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(16), pages 39-50.
    • Handle: RePEc:sgm:jmcbem:v:1:i:16:y:2023:p:39-50
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    References listed on IDEAS

    as
    1. Davide Proserpio & Georgios Zervas, 2017. "Online Reputation Management: Estimating the Impact of Management Responses on Consumer Reviews," Marketing Science, INFORMS, vol. 36(5), pages 645-665, September.
    2. Joachim Büschken & Greg M. Allenby, 2016. "Sentence-Based Text Analysis for Customer Reviews," Marketing Science, INFORMS, vol. 35(6), pages 953-975, November.
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    More about this item

    Keywords

    sentiment analysis; tourism; hotels; marketing; customer’s opinions;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • Z30 - Other Special Topics - - Tourism Economics - - - General

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