Abstract
Job markets are experiencing an exponential growth in data alongside the recent explosion of big data in various domains including health, security and finance. Staying current with job market trends entails collecting, processing and analyzing huge amounts of data. A typical challenge with analyzing job listings is that they vary drastically with regards to verbiage, for instance a given job title or skill can be referred to using different words or industry jargons. As a result, it becomes incumbent to go beyond words present in job listings and carry out analysis aimed at discovering latent structures and trends in job listings. In this paper, we present a systematic approach of uncovering latent trends in job markets using big data technologies (Apache Spark and Scala) and distributed semantic techniques such as latent semantic analysis (LSA). We show how LSA can uncover patterns/relationships/trends that will otherwise remain hidden if using traditional text mining techniques that rely only on word frequencies in documents.