More than words: Social networks’ text mining for consumer brand sentiments

Abstract

Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers’ sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands’ tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.