The purpose of the current research is to develop a methodology that can analyse online reviews using machine learning techniques in such a way that practitioners in the fields of tourism and destination management can understand and apply the technique to improve their attractions. This research studies the TripAdvisor reviews of tourist attractions, including beaches, islands, temples, a pedestrian street, and markets in Phuket, Thailand. In total, 65,079 online reviews were analysed using two machine learning techniques: latent Dirichlet allocation (LDA) and naïve Bayes modelling. LDA modelling helps the researchers determine the dimensions of each type of attraction. Four dimensions were specified for beaches and islands, three dimensions for a pedestrian street and temples, and two dimensions for markets. This research also developed two practical tools – dimensional salience-valence analysis (DSVA) and lexical salience-valence analysis (LSVA) – and used them to suggest actions for the Tourism Authority of Thailand (TAT). •65,079 TripAdvisor reviews of attractions were analysed using machine learning.•10 beaches, 12 island locations, 2 temples, a street, and 12 markets were analysed.•Latent Dirichlet allocation modelling was used to extract dimensions of attractions.•Naïve Bayes modelling was used to analyse underlying terms.