In the last decade, social media platforms have become important communication channels between businesses and consumers. As a result, a lot of consumer-generated data are available online. Unfortunately, they are not fully utilized, partly because of their nature: they are unstructured, subjective, and exist in massive databases. To make use of these data, more than one research method is needed. This study proposes a new, multiple approach to social media data analysis, which counteracts the aforementioned characteristics of social media data. In this new approach the data are first extracted systematically and coded following the principles of content analysis, after a comprehensive literature review has been conducted to guide the coding strategy. Next, the relationships between codes are identified by statistical cluster analysis. These relationships are used in the next step of the analysis, where evaluation criteria weights are derived on the basis of the social media data through probability weighting function. A case study is employed to test the proposed approach.