@article {Naji2022832,
	title = {Psychological Behavior Prediction through Sentiment Analysis Technics: Transformers and ML Approach},
	journal = {Journal of Computer Science},
	volume = {18},
	number = {9},
	year = {2022},
	note = {cited By 0},
	pages = {832-840},
	abstract = {In the era of the COVID-19 epidemic, governments have imposed nationwide lockdowns, which make a huge change to people{\textquoteright}s daily routines. This last impacts indirectly the well-being of people{\textquoteright}s mental health. And due to social media, many conversations about these phenomena occur online, especially those related to people{\textquoteright}s emotions. Which brought challenges and opportunities for sentiment analysis researchers. In this article, we are interested in extracting correlations between this epidemic and its psychological effects by analyzing users{\textquoteright} tweets through common Deep Learning and Machine Learning approaches used for text classification. This last goal is a crucial step to fulfill the main objective of our research: Developing an intelligent system that provides recommendations such as positive support and early alert to help people in case of specific needs particularly challenging mental states {\textcopyright} 2022 Maryame Naji, Najima Daoudi and Rachida Ajhoun},
	doi = {10.3844/jcssp.2022.832.840},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138207279\&doi=10.3844\%2fjcssp.2022.832.840\&partnerID=40\&md5=4450c45ecbc26b88d284f6d19b57a9bd},
	author = {Naji, M. and Daoudi, N. and Ajhoun, R.}
}
@conference {Assami20181265,
	title = {Personalization criteria for enhancing learner engagement in MOOC platforms},
	booktitle = {IEEE Global Engineering Education Conference, EDUCON},
	volume = {2018-April},
	year = {2018},
	pages = {1265-1272},
	doi = {10.1109/EDUCON.2018.8363375},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048130321\&doi=10.1109\%2fEDUCON.2018.8363375\&partnerID=40\&md5=9cfaeac7fd7536644d73cb99c9cbaefc},
	author = {Assami, S. and Daoudi, N. and Ajhoun, R.}
}
