Job Detection in Twitter
This is an incremental application for job category detection in social media data, potentially useful for recruiters or market analysts.
The authors tackled the problem of identifying IT workers from Twitter users by analyzing their tweets, achieving 76% precision and 82% recall using a Skip-gram model.
In this report, we propose a new application for twitter data called \textit{job detection}. We identify people's job category based on their tweets. As a preliminary work, we limited our task to identify only IT workers from other job holders. We have used and compared both simple bag of words model and a document representation based on Skip-gram model. Our results show that the model based on Skip-gram, achieves a 76\% precision and 82\% recall.