Pannee Suanpang, Pitchaya Jamjuntr, Phuripoj Kaewyong
This study aims to use sentiment analysis with a Textblob package for a tourism business case study in Thailand to stimulate the tourism economy post-COVID-19. Sentiment analysis is a text mining technique in a text processing field which is has been an interesting research topic for many years. It is applied to gather tourism information from travellers’ opinions, sentiments, emotions expressed, and attitudes in written language on social media such as review postings, forums, Twitter, blogs, and Facebook. This information is very useful to improve the service of the tourism business. We evaluate performance with Python's TextBlob package with the TripAdvisor dataset from Kaggle that contains 10,000 travellers’ records. Thus, the Native Bayes model in the library is used to analyze tourism sentiments by default. The experimental results show Python's TextBlob package provides 89.32% accuracy which can vary depending on the training data and its suitability to implement for the text mining process in the tourism application.