Abstract
The volume of big data being generated by social network sites (SNS) is increasing significantly. This study seeks to identify the market-applicable insights concerning the text-type big data generated by SNS and to suggest market reaction strategies for responding to signals emerging from big data. Since people can instantly access large amount of online word-of-mouth (e-WoM) contents due to mobile communications, movie sales are influenced significantly from various SNS contents. Based on this phenomenon, we focused on Twitter, one of the most prevalent micro-blogging services. This research conducted a sentiment analysis to determine consumer valences regarding products. This study finds that the extremity of sentiment - as measured by growth speed in the number of positive or negative tweets - changed the direction of the tweets' positive or negative effect on revenue regardless of the valence of the word-of-mouth. The implication for SNS marketing professionals will be discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
