Abstract
We present a newly collected data set of 8,868 gold-standard annotated Arabic feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) ( = 0:816). In addition, the corpus is annotated with a variety of motivated feature-sets that have previously shown positive impact on performance. The paper highlights issues posed by twitter as a genre, such as mixture of language varieties and topic-shifts. Our next step is to extend the current corpus, using online semi-supervised learning. A first sub-corpus will be released via the ELRA repository as part of this submission.- Anthology ID:
- L14-1280
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2268–2273
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/317_Paper.pdf
- DOI:
- Cite (ACL):
- Eshrag Refaee and Verena Rieser. 2014. An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 2268–2273, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis (Refaee & Rieser, LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/317_Paper.pdf