arXiv
Open Access
2017
Job Detection in Twitter
Besat Kassaie
Abstrak
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.
Topik & Kata Kunci
Penulis (1)
B
Besat Kassaie
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2017
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓