There are many valuable insights on jobs and professions in different sectors of society based on their imminent and ascribed characteristics. Studying such characteristics traditionally was done by action research, surveys, questionnaires, etc. which typically take much time and resources to be concluded. In this study we examine vocational education and training data on Twitter. While we present a generic framework to retrieve, process and analyze tweets, we will discuss two research questions from computational social science: First, how can we make Twitter data interoperable to other available resources, e.g. classifications of occupations, tools and skills? Second, do we have enough data to process job collocational prestige analysis on a geographical basis? This presents a novel approach towards labor market research, making novel data interoperable which has not been considered in previous literature. Our approach and pipeline is generic and could be easily extended to other languages. It also contributes to prestige research by widening the question of ascribed prestige to the question how information on occupations is collocated and what these contextualisations tell us about how occupations are seen.