Microsoft Fabric Analytics Engineer (DP-600) Exam Dumps 2024.pdf
The defining features of emotions in online stories
1. The Defining Features of
Emotions in Online Stories
Wen Zhang1
, Yeongbae Choe2
, and Daniel Fesenmaier3
1
University of Central Florida, USA
2
University of Macau, China
3
National Laboratory of Tourism & eCommerce, USA
2. Introduction
• Online storytelling has become a powerful approach to destination promotion
• It builds close emotional connections with tourists through narratives while delivering
information (Tussyadiah and Fesenmaier, 2008; Tussyadiah et al., 2011).
• Various strategies (i.e., plots, etc.) to evoke emotions in the story affect how
readers evaluate the story, which in turn, affects perceptions of the
destination.
• Stories seek to engender different emotions at different times and at different
levels (Reagan, et al., 2016).
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3. Introduction
• There are specific points within the story such as
beginning, peak, end which affect the degree of
emotions created AND overall evaluation.
• These points or rules are often referred to as
heuristics.
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5. Study Purpose
To examine the relationship between key features (e.g. emotion arousal
levels at the beginning, peak and end of the story) and story performance
as measured by the average reading time.
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6. Methodology
• This study examined the emotional structure of 60 online stories which describe
12 states and cities located throughout the United States.
• The stories were designed and implemented during 2017 - 18 by a destination
marketing company and vary in length, focus, logic, and story structure.
• This study adopts a two-stage analytic process: sentiment analysis and multiple
regression analysis.
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9. Methodology
Sentiment Analysis
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Multiple Regression Analysis
1.The reading time for each respective
story was obtained using Google
Analytics
2.The average reading time was
transformed using a natural log
transformation.
11. Discussion
• Key features (e.g. emotion arousal levels at the beginning, peak and end of the
story) significantly correlate with reader involvement in online stories.
• Duration showed non-significant relationship with reading time.
• The findings are important for tourism marketers to design much better online
tourism advertising and destination experience design.
• Advanced machine learning techniques can be used to automatically generate
online stories related to the optimal travel experience, which can be very useful
for the development of more persuasive recommender systems.
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12. Limitation and Future Study
• Investigate the influence of design elements of the website (e.g., pictures, videos,
colour scheme) on reader’s response.
• Combine self-reported and/or physiological data to better capture the effect of
story components on reader’s navigation behaviours.
• Individual-level analysis could provide additional insights on how the structure of
stories influence on the readers’ response to online stories.
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Barbara Tversky – wife of a famous psychologist – Amos Tversky
Barbara Tversky – wife of a famous psychologist – Amos Tversky
The text comprising each story was broken into deciles (10 parts); 10 sentiment scores were generated based on the number of sentences.
Sentiment analyses were first conducted at the sentence level and then aggregated into the deciles for each story.
Various measures of story structure such as emotional level at the start, peak, end, trend and duration were calculated.
The variation of emotions at 20%, 30%, 80%, and maximum sentiment intensity are significant predictors of time spent reading the story;
The length of the story is not a significant predictor;
The results confirm the importance of some key components (start, end, and peak) within the story.
Sentiment analysis can be used to assess the sentiments of online text efficiently, especially when using large volumes of text.