2016
DOI: 10.4236/ajibm.2016.63035
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Social Media Sentimental Analysis in Exhibition’s Visitor Engagement Prediction

Abstract: In the internet age, social media and mobile devices are the most important tools of communication and marketing in the exhibition and event industry. Only a limited research has explored exhibition visitors' engagement and preference perception through social networking media. Our study explores relationship between Facebook fan pages and visitor engagements of the exhibitions. The study found that the number of visitors, Facebook fans like counts, comment counts, and emotional factors (sentiment polarity) ha… Show more

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Cited by 3 publications
(2 citation statements)
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“…There are several approaches proposed to extract information from social media and it can be classified to 2 main techniques as follows: statistical technique and machine learning technique. Linear discriminant analysis (LDA), one of the statistical techniques, was applied to explore the relationship between Facebook fan pages and visitor engagements of the exhibitions [11] and logistic regression was used to analyze textual data from social [12]. Machine Learning was applied to extract information from text data and for example, Gurkhe et al [13] implemented the machine extracted the polarity (positive, negative or neutral) of social media data set by using naive Bayesian technique.…”
Section: Related Workmentioning
confidence: 99%
“…There are several approaches proposed to extract information from social media and it can be classified to 2 main techniques as follows: statistical technique and machine learning technique. Linear discriminant analysis (LDA), one of the statistical techniques, was applied to explore the relationship between Facebook fan pages and visitor engagements of the exhibitions [11] and logistic regression was used to analyze textual data from social [12]. Machine Learning was applied to extract information from text data and for example, Gurkhe et al [13] implemented the machine extracted the polarity (positive, negative or neutral) of social media data set by using naive Bayesian technique.…”
Section: Related Workmentioning
confidence: 99%
“…Referring to Lee, Shia, and Huh (2016), two dimensions of self-satisfaction and group identification for customer value are used in this study.…”
Section: Customer Valuementioning
confidence: 99%