Job Detection in Twitter
Computation and Language
2017-01-12 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1701.03092,
title = {Job Detection in Twitter},
author = {Besat Kassaie},
journal= {arXiv preprint arXiv:1701.03092},
year = {2017}
}