How people utilise tweets on movie selection? The reverse effects of e-WoM valence on movie sales

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.