How to enhance the offline travel intention of the online experience? A big data method based on text-picture

Purpose This paper aims to explore and reveal the factors of online virtual tourism that influence offline travel intention through electronic word-of-mouth. A series of features are extracted from online reviews on the basis of trust theory and sensory theory. Design/methodology/approach A sample of 34,652 reviews from the Airbnb Online Experience were used for a data mining procedure. The topic model and OpenAI algorithm are used to extract and measure the features of Online Experience. The correspondence analysis method is used to understand the mapping relationship between the above features and geographical position. Findings The findings first highlight that the ability, benevolence and integrity attributes of the host can help promote tourist’ offline experience intention, that the saturation and hue indices of the cover picture of Online Experience may increase tourists’ offline travel intention and that the consistency between the cover picture and the experience description can decrease offline travel intention. In addition, this study extracts 11 key features of Online Experience through topic models and location characteristics that enable certain continents’ Online Experience to stand out from others. Originality/value This study extends the influencing factors of offline travel intention using a big data method based on text-picture and assesses the effectiveness of sensory theory and trust theory in investigating tourists’ offline travel intention.