Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach

Authors
B. Venkata Sivaiah1, *, N. Siva2, N. Sunil Kumar3, M. Sucharitha3, S. Yatish Kumar3, Y. Eswar3
1Dept of CSE(DS), Sree Vidyanikethan Engineering College, Tirupati, India
2Dept of CSE, Siddharth Institute of Engineering and Technology, Tirupati, India
3Dept. of CSSE, Sree Vidyanikethan Engineering College, Tirupati, India
*Corresponding author. Email: siva.bheem@hotmail.com
Corresponding Author
B. Venkata Sivaiah
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_69How to use a DOI?
Keywords
Tourism industry; BERT- BiGRU; novelty seeking; online travel reviews
Abstract

An important source of experience-related data for comprehending novelty seeking (NS), a natural personality feature that affects travel motivation and location selection, is online travel reviews. Due to the large number and disorganization of reviews, manually categorizing them is difficult. Therefore, our aim is to develop a deep learning model and classification system to overcome these challenges. We propose a framework that combines four dimensions related to the NS personality trait and use a DL model called BERT-BiGRU to automatically identify NS in TripAdvisor reviews using a dataset of 30,000 reviews. The classifier using the NS multi-dimensional scale and the BERT-BiGRU multi-dimensional scale accurately identified the NS personality trait. It achieved high accuracy and F1 scores. The BERT-BiGRU model outperformed other DL models in terms of accuracy. This study shows how computer methods can be used to automatically determine personality traits from travel reviews. It provides a comprehensive framework for categorizing personality traits in order to benefit marketing and recommendation systems in the travel industry.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
978-94-6463-471-6
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_69How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. Venkata Sivaiah
AU  - N. Siva
AU  - N. Sunil Kumar
AU  - M. Sucharitha
AU  - S. Yatish Kumar
AU  - Y. Eswar
PY  - 2024
DA  - 2024/07/30
TI  - Detecting Novelty Seeking From Online Travel Reviews: A Deep Learning Approach
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 710
EP  - 720
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_69
DO  - 10.2991/978-94-6463-471-6_69
ID  - Sivaiah2024
ER  -