Year: 2014
Type: conference-paper
Abstract: We present a newly collected data set of 8,868 gold-standard annotated Arabic twitter feeds.The corpus is manually labelled for subjectivity and sentiment analysis (SSA) (κ = 0.816).In addition, the corpus is annotated with a variety of linguistically motivated feature-sets that have previously shown positive impact on classification performance.The paper highlights issues posed by twitter as a genre, such as a 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.
Authors: Eshrag Refaee, Verena Rieser
Institution: Heriot-Watt University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2250522473