Assessing Emotions in Online Stories: Comparing Self-report and Text-based Approaches
Abstract (150 Words)
The core product of tourism is experience and therefore is inherently emotional in nature. As such, online storytelling has become an essential destination promotion strategy as it effectively builds emotional connections with potential travelers by incorporating information while encouraging imagination and involvement. Understanding and measuring travelers’ emotional responses to online stories are essential to developing effective advertising strategies. Traditionally, the self-report approach has been employed whereby informants are asked to identify their emotions. With the development of big data, new tools have been developed to detect the sentiment conveyed by text. This paper summarizes the results of a comparative analysis of emotion detection using the self-report method and sentiment analysis. The results show general consistency between the two approaches in terms of overall descriptive statistics as well as the relationships between emotions and various components of advertising response.
Assessing Emotions in Online Stories: Comparing Self-report and Text-based Approaches
The core product of tourism is experience and therefore is inherently emotional in nature. As such, online storytelling has become an essential destination promotion strategy as it effectively builds emotional connections with potential travelers by incorporating information while encouraging imagination and involvement. Understanding and measuring travelers’ emotional responses to online stories are essential to developing effective advertising strategies. Traditionally, the self-report approach has been employed whereby informants are asked to identify their emotions. With the development of big data, new tools have been developed to detect the sentiment conveyed by text. This paper summarizes the results of a comparative analysis of emotion detection using the self-report method and sentiment analysis. The results show general consistency between the two approaches in terms of overall descriptive statistics as well as the relationships between emotions and various components of advertising response.
