no code implementations • 29 Sep 2021 • Hind Alamro, Manal Alshehri, Basma Alharbi, Zuhair Khayyat, Manal Kalkatawi, Inji Ibrahim Jaber, Xiangliang Zhang
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.
no code implementations • 1 Nov 2020 • Basma Alharbi, Hind Alamro, Manal Alshehri, Zuhair Khayyat, Manal Kalkatawi, Inji Ibrahim Jaber, Xiangliang Zhang
This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively.
1 code implementation • ACM SIGKDD international conference on Knowledge discovery and data mining 2015 • Abdulhakim A. Qahtan, Basma Alharbi, Suojin Wang, Xiangliang Zhang
In this paper, we propose a framework for detecting changes in multidimensional data streams based on principal component analysis, which is used for projecting data into a lower dimensional space, thus facilitating density estimation and change-score calculations.