@article {ElKarfi2022561,
	title = {An Ensemble of Arabic Transformer-based Models for Arabic Sentiment Analysis},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {13},
	number = {8},
	year = {2022},
	note = {cited By 0},
	pages = {561-567},
	abstract = {In recent years, sentiment analysis has gained momentum as a research area. This task aims at identifying the opinion that is expressed in a subjective statement. An opinion is a subjective expression describing personal thoughts and feelings. These thoughts and feelings can be assigned with a certain sentiment. The most studied sentiments are positive, negative, and neutral. Since the introduction of attention mechanism in machine learning, sentiment analysis techniques have evolved from recurrent neural networks to transformer models. Transformer-based models are encoder-decoder systems with attention. Attention mechanism has permitted models to consider only relevant parts of a given sequence. Making use of this feature in encoder-decoder architecture has impacted the performance of transformer models in several natural language processing tasks, including sentiment analysis. A significant number of Arabic transformer-based models have been pre-trained recently to perform Arabic sentiment analysis tasks. Most of these models are implemented based on Bidirectional Encoder Representations from Transformers (BERT) such as AraBERT, CAMeLBERT, Arabic ALBERT and GigaBERT. Recent studies have confirmed the effectiveness of this type of models in Arabic sentiment analysis. Thus, in this work, two transformer-based models, namely AraBERT and CAMeLBERT have been experimented. Furthermore, an ensemble model has been implemented to achieve more reasonable performance {\textcopyright} 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.},
	keywords = {Arabic sentiment analyse, Attention mechanisms, Bert, Decoding, Ensemble learning, Learning systems, Performance, Positive/negative, Recurrent neural networks, Research areas, Sentiment analysis, Signal encoding, Transformer, Transformer modeling},
	doi = {10.14569/IJACSA.2022.0130865},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137156665\&doi=10.14569\%2fIJACSA.2022.0130865\&partnerID=40\&md5=927293fb0ea8e16df6981eecff917dd6},
	author = {El Karfi, I. and El Fkihi, S.}
}
@conference {Faizi2017997,
	title = {Extracting business value from big data},
	booktitle = {Proceedings of the 29th International Business Information Management Association Conference - Education Excellence and Innovation Management through Vision 2020: From Regional Development Sustainability to Global Economic Growth},
	year = {2017},
	note = {cited By 0},
	pages = {997-1002},
	abstract = {The exponential growth of structured and unstructured data in the last few years has resulted in the emergence of {\textquoteright}big data{\textquoteright} as one of the hottest technology trends. With data of all kinds being generated in record amounts every minute, big data has become a buzzword across various fields. The objective of this paper is, therefore, to explore the various ways through which businesses can derive value from both internal and external data. In this respect, it was found out that companies, both big and small - across all industries - can use big data to increase customers{\textquoteright} retention and to meet the need of every individual customer. This huge amount of data can also help a business identify the strengths and weaknesses of its competitors and, thus, stay ahead of them. Finally, big data can provide businesses with real-time insights that would enable them to spot or predict problems, and solve them before they happen. Given all these benefits, we strongly believe that the collection and processing of the massive amounts of data, generated by customers or smart devices, can help any enterprise make more powerful and accurate decisions and, thus, significantly improve business operations and organizational performance.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029742611\&partnerID=40\&md5=bfa9daca776ba7dea239abddfc70501a},
	author = {Faizi, R. and El Fkihi, S. and El Afia, A. and Chiheb, R.}
}
