An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis
16783Authors
nan
Abstract
AbstractWe 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.
Keywords
Full Content
nan
Labeled Text
AbstractWe 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.
Publication Details
- Paper ID:
- 16783
- Year:
- 2014
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan