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arXiv · 2109.14456

Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST

Hind Alamro·Manal Alshehri·Basma Alharbi·Zuhair Khayyat·Manal Kalkatawi·Inji Ibrahim Jaber·Xiangliang Zhang
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Abstract

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.

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Hind Alamro, Manal Alshehri, Basma Alharbi, Zuhair Khayyat, Manal Kalkatawi, Inji Ibrahim Jaber, Xiangliang Zhang. 2021-09-29. Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST. https://arxiv.org/abs/2109.14456

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