Simplicity of Positive Reviews and Diversity of Negative Reviews in Hotel Reputation

Sachio Hirokawa, Kiyota Hashimoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    User's review on products and services is valuable information for both users and providers. The present paper conducted a polarity estimation of 73,589 reviews on hotels in Europe. Users rated one to five points for seven aspects (Value, Rooms, Location, Cleanliness, Check-in, Service, Business, Overall). In this paper, we predicted the polarity (positive/negative) of each aspect by using a machine learning method, SVM (Support Vector Machine), and feature selection, with more than 4 points being positive and less than 3 being negative. As a result, positive reviews with respect to six aspects, other than Business, were able to achieve 74% prediction performance (F-measure) with only 20 feature words. On the other hand, for negative reviews, optimal prediction performance could not be obtained unless almost all words were used, and on average F-measure was only 27%. The results indicate that positive reviews are simple, meanwhile negative reviews are diverse and hard to predict mechanically.

    Original languageEnglish
    Title of host publication2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Electronic)9781728101644
    DOIs
    Publication statusPublished - Jul 2 2018
    Event2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Pattaya, Thailand
    Duration: Nov 15 2018Nov 17 2018

    Publication series

    Name2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018 - Proceedings

    Conference

    Conference2018 International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2018
    CountryThailand
    CityPattaya
    Period11/15/1811/17/18

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Software
    • Health Informatics

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