<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The Cappadocia region is one of the most popular tourist destinations in Turkey, and its tourism sector has a significant share in the Turkish economy. In this study, we scraped TripAdvisor reviews of visitors of the Cappadocia region with the Python programming language and used them to analyse public sentiment using various supervised machine learning algorithms. The main purpose of the study is to help create competitive intelligence on both regional and global scales using social media data. For this, we applied Random Forest, Naïve Bayes, and Support Vector Machine methods to classify 4,770 reviews and get insights about the visitors’ perspectives. Results show that the majority of the tourists (90%) had a positive experience during their visit. Most of the complaints focused on the attitudes of staff members. In addition, all three supervised machine learning methods achieved high accuracy in their classification of the reviews. This study is significant in terms of providing a meaningful database for understanding visitor comments, the most important data for the development of tourism in the region, through state-of-the-art machine learning methods, and to direct improvements accordingly.</p>
</abstract>

The Cappadocia region is one of the most popular tourist destinations in Turkey, and its tourism sector has a significant share in the Turkish economy. In this study, we scraped TripAdvisor reviews of visitors of the Cappadocia region with the Python programming language and used them to analyse public sentiment using various supervised machine learning algorithms. The main purpose of the study is to help create competitive intelligence on both regional and global scales using social media data. For this, we applied Random Forest, Naïve Bayes, and Support Vector Machine methods to classify 4,770 reviews and get insights about the visitors’ perspectives. Results show that the majority of the tourists (90%) had a positive experience during their visit. Most of the complaints focused on the attitudes of staff members. In addition, all three supervised machine learning methods achieved high accuracy in their classification of the reviews. This study is significant in terms of providing a meaningful database for understanding visitor comments, the most important data for the development of tourism in the region, through state-of-the-art machine learning methods, and to direct improvements accordingly.

Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists

European Journal of Tourism, Hospitality and Recreation

This work is licensed under the Creative Commons Attribution 4.0 International License.

The Cappadocia region is one of the most popular tourist destinations in Turkey, and its tourism sector has a significant share in the Turkish economy. In this...

Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists

Article Category: Research Article

Pubblicato online: 31 gen 2024

Pagine: 188 - 197

Ricevuto: 06 set 2023

Accettato: 28 ago 2023

DOI: https://doi.org/10.2478/ejthr-2023-0015

Parole chiave
Sentiment Analysis, Supervised Machine Learning, Tourism Industry, Cappadocia, Random Forest, Naïve Bayes

© 2023 Özge Barış-Tüzemen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Sentiment analysis of reviews on cappadocia: The land of beautiful horses in the eyes of tourists

Article Category: Research Article

Pubblicato online: 31 gen 2024

Pagine: 188 - 197

Ricevuto: 06 set 2023

Accettato: 28 ago 2023

DOI: https://doi.org/10.2478/ejthr-2023-0015

Parole chiaveSentiment Analysis, Supervised Machine Learning, Tourism Industry, Cappadocia, Random Forest, Naïve Bayes

© 2023 Özge Barış-Tüzemen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Sentiment Analysis, Supervised Machine Learning, Tourism Industry, Cappadocia, Random Forest, Naïve Bayes