Evaluating Attitude Shift From Tourism Online Reviews Using Word Embedding-Based Approaches

Binyan Chen; Yishi Zhang; Wenjie Chen

Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China

Research Article

Evaluating Attitude Shift From Tourism Online Reviews Using Word Embedding-Based Approaches

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@INPROCEEDINGS{10.4108/eai.18-11-2022.2327170,
    author={Binyan  Chen and Yishi  Zhang and Wenjie  Chen},
    title={Evaluating Attitude Shift From Tourism Online Reviews Using Word Embedding-Based Approaches},
    proceedings={Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China},
    publisher={EAI},
    proceedings_a={ICEMME},
    year={2023},
    month={2},
    keywords={online reviews; review usefulness; sentiment analysis; word embedding; deep learning},
    doi={10.4108/eai.18-11-2022.2327170}
}

Binyan Chen
Yishi Zhang
Wenjie Chen
Year: 2023
Evaluating Attitude Shift From Tourism Online Reviews Using Word Embedding-Based Approaches
ICEMME
EAI
DOI: 10.4108/eai.18-11-2022.2327170

Binyan Chen¹^,*, Yishi Zhang¹, Wenjie Chen¹

1: Wuhan University of Technology

*Contact email: 295289@whut.edu.cn

Abstract

The shift of consumers' attitudes caused by public health emergencies can be reflected in online reviews. This study applies the word embedding-based methods and sentiment propensity analysis to mine tourism online reviews. Specifically, we obtain the comprehensive vector of each review, which consists of the word vectors in the review word embedding, and evaluate the changes in tourists' emotional tendencies in the context of public health emergencies (taking COVID-19 as an example). The empirical results verify the effectiveness of the word embedding-based methods in identifying attitude shift and confirm the divergent effect of emotional valence in predicting the usefulness of tourism online reviews.

Keywords: online reviews; review usefulness; sentiment analysis; word embedding; deep learning

Published: 2023-02-15
Publisher: EAI

: http://dx.doi.org/10.4108/eai.18-11-2022.2327170