Sentiment classification and visualization analysis of tourism comments: A canton tower example

Research Article
Open access

Sentiment classification and visualization analysis of tourism comments: A canton tower example

Yanping Lin 1*
  • 1 South China Agricultural University    
  • *corresponding author guaji_xd@stu.scau.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/19/20231021
ACE Vol.19
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-029-5
ISBN (Online): 978-1-83558-030-1

Abstract

As calculated by the Ministry of Culture and Tourism, there were 308 million domestic tourist trips in China during the Spring Festival in 2023, witnessing a year-on-year increase of 23.1%. And the satisfaction of tourists with certain spots can be partly reflected in the comments and scores they made on social media. Therefore, this research was aimed at mining useful information from the comment and scores of Canton Tower. After collecting detailed comment information from the web, this research used the plot module of Python to make data visualization to observe the distribution of users’ location, comment time, and comment label as well as the word cloud of remarks. Then the research used the data set to train three different sentiment analysis models including Naïve Beyas, SnowNLP, and Bert, then compared their accuracy in predicting. This research shows that over half of the comments came from Guangdong Province, most of the tourists were content with Canton Tower, and the number of comments has increased obviously since 2023. In addition, the research found that the model having the highest accuracy of sentiment analysis is the Bert model, about 90%.

Keywords:

sentiment analysis, sentiment information classification, data visualization, online comment

Lin,Y. (2023). Sentiment classification and visualization analysis of tourism comments: A canton tower example. Applied and Computational Engineering,19,132-138.
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References

[1]. Seyfi, S., Hall, C. M., & Shabani, B. (2023). COVID-19 and international travel restrictions: the geopolitics of health and tourism. Tourism Geographies, 25(1), 357-373.

[2]. Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7, 51522-51532.

[3]. Alrumaih, A., Al-Sabbagh, A., Alsabah, R., Kharrufa, H., & Baldwin, J. (2020). Sentiment analysis of comments in social media. International Journal of Electrical & Computer Engineering 10(6), 2088-8708.

[4]. Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7, 51522-51532.

[5]. Yang, X., Xu, S., Wu, H., & Bie, R. (2019). Sentiment analysis of Weibo comment texts based on extended vocabulary and convolutional neural network. Procedia computer science, 147, 361-368.

[6]. Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), 45-49.

[7]. Efron, B. (2013). Bayes' theorem in the 21st century. Science, 340(6137), 1177-1178.

[8]. Chen, C., Chen, J., & Shi, C. (2018). Research on credit evaluation model of online store based on SnowNLP. In E3S Web of Conferences, 53, 03039.

[9]. Lin, Y., Chen, L., & Zhang, C. (2022). Analysis of Tourist Hotel Impression Based on SnowNLP Model. In 2nd International Conference on Internet, Education and Information Technology, 373-378.

[10]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Cite this article

Lin,Y. (2023). Sentiment classification and visualization analysis of tourism comments: A canton tower example. Applied and Computational Engineering,19,132-138.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-029-5(Print) / 978-1-83558-030-1(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.19
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Seyfi, S., Hall, C. M., & Shabani, B. (2023). COVID-19 and international travel restrictions: the geopolitics of health and tourism. Tourism Geographies, 25(1), 357-373.

[2]. Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7, 51522-51532.

[3]. Alrumaih, A., Al-Sabbagh, A., Alsabah, R., Kharrufa, H., & Baldwin, J. (2020). Sentiment analysis of comments in social media. International Journal of Electrical & Computer Engineering 10(6), 2088-8708.

[4]. Xu, G., Meng, Y., Qiu, X., Yu, Z., & Wu, X. (2019). Sentiment analysis of comment texts based on BiLSTM. Ieee Access, 7, 51522-51532.

[5]. Yang, X., Xu, S., Wu, H., & Bie, R. (2019). Sentiment analysis of Weibo comment texts based on extended vocabulary and convolutional neural network. Procedia computer science, 147, 361-368.

[6]. Baid, P., Gupta, A., & Chaplot, N. (2017). Sentiment analysis of movie reviews using machine learning techniques. International Journal of Computer Applications, 179(7), 45-49.

[7]. Efron, B. (2013). Bayes' theorem in the 21st century. Science, 340(6137), 1177-1178.

[8]. Chen, C., Chen, J., & Shi, C. (2018). Research on credit evaluation model of online store based on SnowNLP. In E3S Web of Conferences, 53, 03039.

[9]. Lin, Y., Chen, L., & Zhang, C. (2022). Analysis of Tourist Hotel Impression Based on SnowNLP Model. In 2nd International Conference on Internet, Education and Information Technology, 373-378.

[10]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

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