[Objective] This study aims to find the temporal-distribution patterns of tourists’ attitudes towards their destinations through sentiment analysis of travel blogs. [Context] More and more tourists collect information on their destinations from travel blogs, which provide enormous business opportunities. [Methods] We proposed a sentiment analysis model based on temporal characteristics of travel blogs. It includes the following modules: data collection, preprocessing, identifying sentiment words, weight calculation, and analysis. The model was examined with four types of travel blogs. [Results] The number of post with “good” emotion was always higher than others each month. The volatility of “good”, “happiness” and “disgust” emotion was the highest in different months. The volatility emotion over time was not correlated to the number of related travel blogs. There is no relationship between the peak/off seasons and the emotion of tourists. [Conclusions] The proposed model could identify the changing of tourist sentiment over time, which provides new information for tourism managers and potential visitors.
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