  This paper provides an overview of the Arabic Sentiment Analysis Challenge
organized by King Abdullah University of Science and Technology (KAUST). The
task in this challenge is to develop machine learning models to classify a
given tweet into one of the three categories Positive, Negative, or Neutral.
From our recently released ASAD dataset, we provide the competitors with 55K
tweets for training, and 20K tweets for validation, based on which the
performance of participating teams are ranked on a leaderboard,
https://www.kaggle.com/c/arabic-sentiment-analysis-2021-kaust. The competition
received in total 1247 submissions from 74 teams (99 team members). The final
winners are determined by another private set of 20K tweets that have the same
distribution as the training and validation set. In this paper, we present the
main findings in the competition and summarize the methods and tools used by
the top ranked teams. The full dataset of 100K labeled tweets is also released
for public usage, at
https://www.kaggle.com/c/arabic-sentiment-analysis-2021-kaust/data.
