Social Media Intelligence: Measuring Brand Sentiment from Online Conversations

Abstract

With the proliferation of social media, questions have begun to emerge about its role in providing marketing insights. In this research, we investigate the potential to “listen in” on social media conversations as a means of inferring brand sentiment. Our analysis employs data collected from multiple website domains, spanning a variety of online venue formats to which social media comments may be contributed. We demonstrate how factors relating to the focus of social media comments and the venue to which they have been contributed need to be explicitly modeled when deriving measures of online brand sentiment. Thus, we propose a model that separates the underlying brand sentiment from the effects of other predictable factors on social media comments. We apply our model to data pertaining to a leading enterprise software brand and show how our proposed approach provides an adjusted brand sentiment metric that is correlated with the results of an offline brand tracking survey. In contrast, a simple average of sentiment across all social media comments is uncorrelated with the same offline tracking survey. We also apply our modeling framework to social media comments related to three brands in different industries. From these analyses, we further demonstrate the potential pitfalls associated with simple average sentiment measures. We conclude by discussing the implications of our findings for practitioners who are considering social media as a potential research tool.