Social media platforms as enabler for real time and many-to-many communication play an important role in the analysis of consumers’ opinions, attitudes, moods, and behaviors towards brands. Emojis as a non-verbal, explanatory and emotional component are increasingly used for a more expressive online communication. While current emotion mining tools only focus on text analysis, we are the first who conduct an automated sentiment analysis of brand-related tweets containing emojis in addition to text. We therefore analyzed 999,197 Starbucks-related and 566,597 McDonald’s-related tweets. We used tweets directed at two different global brands in the fast food sector to increase generalizability. On a sentiment polarity scale, the analyzed tweets show a rather positive sentiment value towards Starbucks and a slightly negative sentiment value towards McDonald’s. We also find that sentiment is classified identically across brands for 94\% of emojis. We conclude that the sentiment value can be considered as an indicator for the perceived image of a brand. Our approach provides an innovative tool for companies to directly analyze emotional content on social media platforms and improves the understanding for the needs of consumers.
The research gap and the scientific approach were predetermined by the Chair of Marketing at Mercator School of Management, University of Duisburg-Essen.