Name: AT-ODTSA
Description: A dataset of Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets (topics) and sentiments (opinions) expressed in the collected tweets.
Provider: Multiple Institutions 
Paper Title: AT-ODTSA: a Dataset of Arabic Tweets for Open Domain Targeted Sentiment Analysis
Tasks: open-domain targeted sentiment Analysis
Venue Name: International Journal of Computing and Digital Systems
Authors: Shaaban Sahmoud, Shadi Abudalfa, Wisam Elmasry
Abstract: In the field of sentiment analysis, most of research has conducted experiments on datasets collected from Twitter for
  manipulating a specific language. Little number of datasets has been collected for detecting sentiments expressed in Arabic tweets.
  Moreover, very limited number of such datasets is suitable for conducting recent research directions such as target dependent sentiment
  analysis and open-domain targeted sentiment analysis. Thereby, there is a dire need for reliable datasets that are specifically acquired
  for open-domain targeted sentiment analysis with Arabic language. Therefore, in this paper, we introduce AT-ODTSA, a dataset of
  Arabic Tweets for Open-Domain Targeted Sentiment Analysis, which includes Arabic tweets along with labels that specify targets
  (topics) and sentiments (opinions) expressed in the collected tweets. To the best of our knowledge, our work presents the first dataset
  that manually annotated for applying Arabic open-domain targeted sentiment analysis. We also present a detailed statistical analysis of
  the dataset. The AT-ODTSA dataset is suitable for train numerous machine learning models such as a deep learning-based model.