To the top

Page Manager: Webmaster
Last update: 9/11/2012 3:13 PM

Tell a friend about this page
Print version

An Arabic Tweets Sentimen… - University of Gothenburg, Sweden Till startsida
Sitemap
To content Read more about how we use cookies on gu.se

An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training

Conference paper
Authors Chatrine (kathrein) Qwaider (abu kwaik)
Stergios Chatzikyriakidis
Simon Dobnik
Richard Johansson
Motaz Saad
Published in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools with a Shared Task on Offensive Language Detection (OSACT4-2020) at Language Resources and Evaluation Conference (LREC 2020)
ISBN 979-10-95546-51-1
Publisher European Language Resources Association (ELRA)
Place of publication Marseille, France
Publication year 2020
Published at Department of Computer Science and Engineering (GU)
Department of Philosophy, Linguistics and Theory of Science
Language en
Links edinburghnlp.inf.ed.ac.uk/workshops...
https://www.aclweb.org/anthology/20...
https://gup.ub.gu.se/file/208188
Keywords natural language processing, sentiment analysis, distant supervision, self training
Subject categories Computational linguistics, Linguistics, Arabic language

Abstract

As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions. For good social media sentiment analysis, good quality resources are needed, and the lack of these resources is particularly evident for languages other than English, in particular Arabic. The available Arabic resources lack of from either the size of the corpus or the quality of the annotation. In this paper, we present an Arabic Sentiment Analysis Corpus collected from Twitter, which contains 36K tweets labelled into positive and negative. We employed distant supervision and self-training approaches into the corpus to annotate it. Besides, we release an 8K tweets manually annotated as a gold standard. We evaluated the corpus intrinsically by comparing it to human classification and pre-trained sentiment analysis models. Moreover, we apply extrinsic evaluation methods exploiting sentiment analysis task and achieve an accuracy of 86%.

Page Manager: Webmaster|Last update: 9/11/2012
Share:

The University of Gothenburg uses cookies to provide you with the best possible user experience. By continuing on this website, you approve of our use of cookies.  What are cookies?