Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST
It addresses sentiment analysis for Arabic tweets, providing a benchmark and dataset for the research community, but is incremental as it focuses on summarizing an existing competition.
The paper describes the Arabic Sentiment Analysis 2021 Competition at KAUST, which involved developing machine learning models to classify tweets into positive, negative, or neutral categories, resulting in 1247 submissions from 74 teams and the release of a 100K tweet dataset.
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.