Abstract
We study the discursive practices of politicians and journalists on social media. For this we need more annotated data than we currently have but the annotation process is time-consuming and costly. In this paper we examine machine learning methods for automatically annotating unseen tweetsbased on a small set of manually annotated tweets. Forimproving the performance of the learner, we focus onmethods related to training data expansion, like artificialtraining data, active learning and incorporating languagemodels developed from unannotated text.
Original language | English |
---|---|
Title of host publication | Proceedings - 13th IEEE International Conference on eScience, eScience 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 438-439 |
Number of pages | 2 |
ISBN (Electronic) | 9781538626863 |
DOIs | |
Publication status | Published - 14-Nov-2017 |
Event | 13th IEEE International Conference on eScience, eScience 2017 - Auckland, New Zealand Duration: 24-Oct-2017 → 27-Oct-2017 |
Conference
Conference | 13th IEEE International Conference on eScience, eScience 2017 |
---|---|
Country/Territory | New Zealand |
City | Auckland |
Period | 24/10/2017 → 27/10/2017 |
Keywords
- machine learning
- political science
- social media