Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

Adri Priadana, Ahmad Ashril Rizal

Abstract


The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Keywords


Sentiment Analysis;Government Performance in Tourism;COVID-19 Pandemic Period;Lexicon Based

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References


A. Priadana and M. Habibi, “Face Detection using Haar Cascades to Filter Selfie Face Image on Instagram,” in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, pp. 6–9, doi: 10.1109/ICAIIT.2019.8834526.

M. I. Akrianto, A. D. Hartanto, and A. Priadana, “The Best Parameters to Select Instagram Account for Endorsement using Web Scraping,” in 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2019, pp. 40–45, doi: 10.1109/ICITISEE48480.2019.9004038.

M. N. Fatanti and I. W. Suyadnya, “Beyond User Gaze: How Instagram Creates Tourism Destination Brand?,” Procedia - Soc. Behav. Sci., vol. 211, pp. 1089–1095, Nov. 2015, doi: 10.1016/j.sbspro.2015.11.145.

M. L. Yadav and B. Roychoudhury, “Effect of trip mode on opinion about hotel aspects: A social media analysis approach,” Int. J. Hosp. Manag., vol. 80, no. September 2018, pp. 155–165, 2019, doi: 10.1016/j.ijhm.2019.02.002.

C. Bucur, “Using Opinion Mining Techniques in Tourism,” Procedia Econ. Financ., vol. 23, no. October 2014, pp. 1666–1673, 2015, doi: 10.1016/s2212-5671(15)00471-2.

S. Ainin, A. Feizollah, N. B. Anuar, and N. A. Abdullah, “Sentiment analyses of multilingual tweets on halal tourism,” Tour. Manag. Perspect., vol. 34, no. January 2019, p. 100658, 2020, doi: 10.1016/j.tmp.2020.100658.

C. Shofiya and S. Abidi, “Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data,” Int. J. Environ. Res. Public Heal. 2021, Vol. 18, Page 5993, vol. 18, no. 11, p. 5993, Jun. 2021, doi: 10.3390/IJERPH18115993.

R. Obiedat, O. Harfoushi, R. Qaddoura, L. Al-Qaisi, and A. M. Al-Zoubi, “An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan,” Appl. Sci. 2021, Vol. 11, Page 9080, vol. 11, no. 19, p. 9080, Sep. 2021, doi: 10.3390/APP11199080.

M. Habibi, A. Priadana, and M. R. Ma’arif, “Sentiment Analysis and Topic Modeling of Indonesian Public Conversation about COVID-19 Epidemics on Twitter,” IJID (International J. Informatics Dev., vol. 10, no. 1, pp. 23–30, Jun. 2021, doi: 10.14421/IJID.2021.2400.

P. H. Prastyo, A. S. Sumi, A. W. Dian, and A. E. Permanasari, “Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel,” J. Inf. Syst. Eng. Bus. Intell., vol. 6, no. 2, pp. 112–122, Oct. 2020, doi: 10.20473/JISEBI.6.2.112-122.

M. Youssef and S. R. El-Beltagy, “MoArLex: An Arabic Sentiment Lexicon Built Through Automatic Lexicon Expansion,” Procedia Comput. Sci., vol. 142, pp. 94–103, 2018, doi: 10.1016/j.procs.2018.10.464.

Z. Drus and H. Khalid, “Sentiment analysis in social media and its application: Systematic literature review,” Procedia Comput. Sci., vol. 161, pp. 707–714, 2019, doi: 10.1016/j.procs.2019.11.174.

Z. Jianqiang and G. Xiaolin, “Comparison research on text pre-processing methods on twitter sentiment analysis,” IEEE Access, vol. 5, no. c, pp. 2870–2879, 2017, doi: 10.1109/ACCESS.2017.2672677.

E. Ferrara, P. De Meo, G. Fiumara, and R. Baumgartner, “Web data extraction, applications and techniques: A survey,” Knowledge-Based Syst., vol. 70, pp. 301–323, Nov. 2014, doi: 10.1016/j.knosys.2014.07.007.

S. Rana and A. Singh, “Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques,” Proc. 2016 2nd Int. Conf. Next Gener. Comput. Technol. NGCT 2016, no. October, pp. 106–111, 2017, doi: 10.1109/NGCT.2016.7877399.

E. M. Martín and Á. P. del Pobil, Robust Motion Detection in Real-Life Scenarios, 1st ed. Springer-Verlag London, 2012.




DOI: https://doi.org/10.18860/ca.v7i1.12488

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