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What Do Twitter Users Think of Top Fast Food Brands?: Exploring Public Opinion Using a Sentiment Analysis Approach
Abstract
This research study explores how some of the largest American fast-food brands are perceived globally through the analysis of 10,002 tweets to identify insights and build knowledge around the industry. With this exploratory research, the thesis aims to ultimately provide recommendations in the digital scope that could improve the public opinion of negatively perceived brands. The methodology in this research consists of a data-driven process in which three different analyses are carried out: sentiment analysis, textual analysis, and a content analysis of each brand. By collecting data from user-generated content on Twitter, the sentiment of each tweet has been classified with an algorithm developed and trained specifically for this research. According to Krippendorff's alpha value, the results have a tentative conclusive reliability, indicating that McDonald's and Starbucks have the highest percentage of negatively classified tweets. To improve their negative perception online, it has been recommended that they showcase different types of content on their digital platforms.
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