Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST

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.

[1]  Preslav Nakov,et al.  SemEval-2017 Task 4: Sentiment Analysis in Twitter , 2017, *SEMEVAL.

[2]  Ahmed Khoumsi,et al.  Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language , 2021, WANLP.

[3]  K. P. Singh,et al.  Analysis of Political Sentiment Orientations on Twitter , 2020 .

[4]  Deniz Yuret,et al.  KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media , 2020, SEMEVAL.

[5]  Muhammad Abdul-Mageed,et al.  ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic , 2020, ACL.

[6]  Mark Lee,et al.  Enhancing Contextualised Language Models with Static Character and Word Embeddings for Emotional Intensity and Sentiment Strength Detection in Arabic Tweets , 2021, ACLING.

[7]  Mohamad Ivan Fanany,et al.  Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers , 2015 .

[8]  Jyoti Ramteke,et al.  Election result prediction using Twitter sentiment analysis , 2016, 2016 International Conference on Inventive Computation Technologies (ICICT).

[9]  Walid Magdy,et al.  From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset , 2020, OSACT.

[10]  Mark Lee,et al.  Combining Character and Word Embeddings for Affect in Arabic Informal Social Media Microblogs , 2020, NLDB.

[11]  Xiangliang Zhang,et al.  SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic , 2020, ArXiv.

[12]  Indra Budi,et al.  Twitter sentiment analysis of online transportation service providers , 2016, 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[13]  Ibrahim Abu Farha,et al.  Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic , 2021, WANLP.

[14]  Wei Wang,et al.  Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic , 2021 .

[15]  Ismail Berrada,et al.  CS-UM6P at SemEval-2021 Task 7: Deep Multi-Task Learning Model for Detecting and Rating Humor and Offense , 2021, SEMEVAL.

[16]  Jennifer Foster,et al.  Sentiment Analysis of Political Tweets: Towards an Accurate Classifier , 2013 .

[17]  Kalpdrum Passi,et al.  Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning , 2020, IoT.

[18]  Abdelkader El Mahdaouy,et al.  BERT-based Multi-Task Model for Country and Province Level MSA and Dialectal Arabic Identification , 2021, WANLP.

[19]  Zhiyong Luo,et al.  Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts , 2016, COLING.

[20]  Hazem Hajj,et al.  AraBERT: Transformer-based Model for Arabic Language Understanding , 2020, OSACT.

[21]  Erik Cambria,et al.  A review of sentiment analysis research in Arabic language , 2020, Future Gener. Comput. Syst..