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Publication
Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.
We present an automatic method for trend detection in job ads. From a job-posting website, we collect job ads from 16 countries and in 8 languages and 6 job domains. We pre-process them by removing stop words, lemmatising and performing cross-domain filtering. Then, we improve the vocabulary by forming n-grams and restrict it by filtering based on named-entity and part-of-speech tags. We split the job ads to compare two time periods: the first halves of 2016 and 2017. A trending word is defined as a word with a higher TF-IDF weight in 2017 than in 2016. The results obtained show a close correlation between the position of a word in its text and its trendiness regardless of country, language or job domain.
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