ASTD: Arabic Sentiment Tweets Dataset
By Mahmoud Nabil, Mohamed A. Aly, Amir F. Atiya. Conference on Empirical Methods in Natural Language Processing, 2015
357 citations recorded by the source corpus.
This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run experiments using standard partitioning of the dataset. Our experiments provide benchmark results for 4 way sentiment classification on the dataset.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/fb4dc213d0f3c5c39bdd4db62fb1aa227934fd64
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Muhammad Abdul-Mageed
- AbdelRahim Elmadany
- El Moatez Billah Nagoudi
- Bashar Alhafni
- Houda Bouamor
- Nizar Habash
- Fady Baly
- Hazem M. Hajj
In Information Systems
Citations in the corpus
- ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic (2020, methodology): “These are (1) sentiment analysis from six different datasets: HARD (Elnagar et al., 2018), ASTD (Nabil et al., 2015), ArsenTD-Lev (Baly et al., 2019), LABR (Aly and Atiya, 2013), and ArSaS (Elmadany et al., 2018).”
- AraBERT: Transformer-based Model for Arabic Language Understanding (2020, background): “The Arabic Sentiment Twitter Dataset (Nabil et al., 2015) contains 10,000 tweets written in both MSA and Egyptian dialect.”
- The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models (2021, methodology): “Experimental Setup Our models were fine-tuned on ArSenTD-Lev and the train splits from SemEval, ASTD, and ArSAS (23,327 tweets) on a single GPU for 3 epochs with a learning rate of 3e-5, batch size of 32, and a maximum sequence length of 128.”