# Job Detection in Twitter

## TL;DR

This paper introduces a novel application called job detection on Twitter, focusing on identifying IT workers from other job categories using text analysis models, with promising results from Skip-gram based representations.

## Contribution

It presents a new approach for job detection in social media data, comparing simple bag of words and Skip-gram models for improved classification accuracy.

## Key findings

- Skip-gram model achieves 76% precision
- Skip-gram model achieves 82% recall
- Preliminary results show effectiveness of the approach

## 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.