Abstract
ABSTRACT Online texts and images have become an important data source for investigating tourism emotion. However, tourism emotion extracted from online data cannot reflect the emotions in the real world. Few scholarly attempts have focused on the complex biases in tourism emotion based on online data. By comparing online and offline emotion, this study quantified biases in tourism emotion and explored patterns among biases. Facial expressions and texts within Disney Resort were collected. Based on facial expression recognition and text mining, several indices were proposed to measure tourism emotion and biases. Our results reveal that tourists tend to share stronger happiness, increased anger in commerce areas and exaggerated disgust in waiting areas. In addition, males show “surprise-suppress”; while females show “surprise-exaggeration” in recreation areas. Our research can help scholars reexamine previous conclusions based on online tourism emotion and provides a theoretical basis for improving the quality of online tourism emotion.
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