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

Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik, Motaz Saad, Richard Johansson


Abstract
As the number of social media users increases, they express their thoughts, needs, socialise and publish their opinions reviews. 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%.
Anthology ID:
2020.osact-1.1
Volume:
Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
OSACT
SIG:
Publisher:
European Language Resource Association
Note:
Pages:
1–8
Language:
English
URL:
https://aclanthology.org/2020.osact-1.1
DOI:
Bibkey:
Cite (ACL):
Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik, Motaz Saad, and Richard Johansson. 2020. An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training. In Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection, pages 1–8, Marseille, France. European Language Resource Association.
Cite (Informal):
An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training (Abu Kwaik et al., OSACT 2020)
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PDF:
https://aclanthology.org/2020.osact-1.1.pdf