Preoţiuc-Pietro, D;
Lampos, V;
Aletras, N;
(2015)
An analysis of the user occupational class through Twitter content.
In:
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing : Volume 1: Long Papers.
The Association for Computational Linguistics
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Abstract
Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.
| Type: | Proceedings paper |
|---|---|
| Title: | An analysis of the user occupational class through Twitter content |
| Event: | Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics |
| ISBN: | 9781941643723 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://www.aclweb.org/anthology/P/P15/ |
| Language: | English |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1467985 |
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