Twitter data sentiment analysis of tourism in Thailand during the COVID-19 pandemic using machine learning
Heliyon
; 2022.
Article
in English
| EuropePMC | ID: covidwho-2045973
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has severely affected Thailand's economy, which relies heavily on tourism. In this study, we labeled the sentiment and intention classes of English-language tweets related to tourism in Bangkok, Chiang Mai, and Phuket. Then, the accuracy of three machine learning algorithms (decision tree, random forest, and support vector machine) in predicting the sentiments and intentions of the tweets was investigated. The support vector machine algorithm provided the best results for sentiment analysis, with a maximum accuracy of 77.4%. In the intention analysis, the random forest algorithm achieved an accuracy of 95.4%. In a subsequent preliminary qualitative content analysis, the top 10 words found in each sentiment and intention class were gathered to provide insights and suggestions to help increase tourism in Thailand. The results of this study suggest that to help restore tourism in Thailand, tourist destinations, natural attractions, restaurants, and nightlife should be promoted. In addition, the two main concerns of tourists to Thailand should be addressed COVID-19 and current political tensions. COVID19, Machine learning, Sentiment analysis, Tweet, Tourism, Thailand.
Search on Google
Collection:
Databases of international organizations
Database:
EuropePMC
Language:
English
Journal:
Heliyon
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS