@article {ElHaddaoui2022529,
	title = {On the Sensitivity of LSTMs to Hyperparameters and Word Embeddings in the Context of Sentiment Analysis},
	journal = {Lecture Notes in Networks and Systems},
	volume = {489 LNNS},
	year = {2022},
	note = {cited By 0},
	pages = {529-542},
	abstract = {Recurrent neural networks are still providing excellent results in sentiment analysis tasks, variants such as LSTM and Bidirectional LSTM have become a reference for building fast and accurate predictive models. However, such performance is difficult to obtain due to the complexity of the models and the hyperparameters choice. LSTM based models can easily overfit to the studied domain, and tuning the hyperparameters to get the desired model is the keystone of the training process. In this work, we provide a study on the sensitivity of a selection of LSTM based models to various hyperparameters and we highlight important aspects to consider while using similar models in the context of sentiment analysis. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-031-07969-6_40},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135093996\&doi=10.1007\%2f978-3-031-07969-6_40\&partnerID=40\&md5=695a2003d02e28d6d72df06cca749381},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
@article {Faizi202252,
	title = {A Sentiment Analysis Based Approach for Exploring Student Feedback},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13449 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {52-59},
	abstract = {Student feedback is commonly used as a reliable source of information to evaluate learning outcomes and teaching quality. This feedback has proven to provide faculty not only with valuable insights into how students are learning, but also with an ideal opportunity to reflect on teaching resources and instructional strategies. However, given the increasing massive amounts of feedback that is available online, collecting and analyzing this data manually is not usually an easy task. The aim of this work is, therefore, to put forward a sentiment analysis classifier that is capable of categorizing student feedback as being either positive or negative. To this end, students{\textquoteright} reviews posted about online courses were automatically extracted, preprocessed and then fed into various machine learning algorithms. The findings of this analysis revealed that the Support Vector Machines (SVM) algorithm achieves the highest accuracy score (93.35\%) and, thus, outperforms other implemented models. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Analysis-based approaches, Education computing, Learning algorithms, Learning outcome, Machine-learning, Outcome quality, Quality control, Sentiment analysis, Sources of informations, Student feedback, Students, Support vector machines, Support vectors machine, Teaching quality, Teaching resources},
	doi = {10.1007/978-3-031-15273-3_6},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137997385\&doi=10.1007\%2f978-3-031-15273-3_6\&partnerID=40\&md5=feac3688f39fab9ad1f85464953459ec},
	author = {Faizi, R. and El Fkihi, S.}
}
@article {Faizi2022549,
	title = {A Sentiment-Based Approach to Predict Learners{\textquoteright} Perceptions Towards YouTube Educational Videos},
	journal = {Lecture Notes in Networks and Systems},
	volume = {419 LNNS},
	year = {2022},
	note = {cited By 1},
	pages = {549-556},
	abstract = {Given the increasing proliferation of educational resources on YouTube, this social media platform has become an optimal space to which students usually resort to improve their learning in various subjects. It has also turned into an online community in which all users can voice their opinion about videos they have watched. However, getting the perceptions of learners on these learning materials is not always an easy task. Our objective in this paper is, therefore, to propose a machine learning approach that can classify learners{\textquoteright} feedback as being positive or negative. For this purpose, textual data was retrieved from YouTube educational videos, pre-processed and then classified using different machine learning algorithms. Results of the present study demonstrated that the Support Vector Machines algorithm performs better than other models. In fact, with an accuracy score of 92.82\% on combinations of unigrams and bigrams and 92.67\% for associations of unigrams and trigrams, this supervised machine learning approach outperforms other implemented models, namely Na{\"\i}ve Bayes, Random Forest and Logistic Regression. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	doi = {10.1007/978-3-030-96299-9_52},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126255114\&doi=10.1007\%2f978-3-030-96299-9_52\&partnerID=40\&md5=488d0e3f5f3597e45e73566a94ba74a7},
	author = {Faizi, R.}
}
@article {ElHaddaoui2021336,
	title = {Sentiment analysis: A review and framework foundations},
	journal = {International Journal of Data Analysis Techniques and Strategies},
	volume = {13},
	number = {4},
	year = {2021},
	note = {cited By 0},
	pages = {336-355},
	abstract = {The rise of social media as a platform for opinion expression and social interactions motivated the need for an automated data analysis technique for business value extraction with optimal investment considerations. In this respect, sentiment analysis (SA) becomes the de facto approach to investigate generated data and retrieve information such as sentiments and emotions, discussed topics, etc., via traditional machine learning and modern neural network-based algorithms. The current techniques achieve reasonable accuracy scores but their performance evolution is depending on the context of application, also most implementations are complex and non-reusable components. Our literature review shows a lack in research studies to unify existing systems under a common framework for SA tasks. This paper also highlights the rending movement of neural networks approaches and pinpoint recent research studies for SA sub tasks. A SA framework design proposition is presented based on key research projects and enhanced with other promising works. {\textcopyright} 2021 Inderscience Publishers. All rights reserved.},
	doi = {10.1504/IJDATS.2021.120112},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122875189\&doi=10.1504\%2fIJDATS.2021.120112\&partnerID=40\&md5=e3d2033b898f101c2ec5d087fdf6472e},
	author = {El Haddaoui, B. and Chiheb, R. and Faizi, R. and El Afia, A.}
}
@article {Boudad20182479,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	volume = {9},
	number = {4},
	year = {2018},
	pages = {2479-2490},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@conference {ElKarfi201859,
	title = {A spectral clustering-based approach for sentiment classification in modern standard Arabic},
	booktitle = {MCCSIS 2018 - Multi Conference on Computer Science and Information Systems; Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2018, Theory and Practice in Modern Computing 2018 and Connected Sma},
	year = {2018},
	pages = {59-65},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054167622\&partnerID=40\&md5=101982324dd788664f052d2919012106},
	author = {El Karfi, I. and El Fkihi, S. and Faizi, R.}
}
@conference {Faizi20171760,
	title = {Screening job candidates via social media: Opportunities and pitfalls},
	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 = {1760-1765},
	abstract = {Many businesses have undergone significant changes over the past decade due to the exponential growth of social media. These technologies have not only altered how professionals and businesses operate but also the way they get engaged with target groups. With millions of job seekers being tech-savvy, recruiters are heavily using social media to screen job candidates. Whether seeking a job or hunting for candidates, these online networks seem to be the ideal venue. Therefore, our objective in this paper is to examine the opportunities and pitfalls of using social media in the recruitment process. In this respect, it was found out that these online platforms enable human resources managers to have access to a large and diverse pool of qualified prospective employees at a lower cost, and to get connected with passive candidates. Social media also provide recruiters with an opportunity to get greater insight into an applicant{\textquoteright}s real abilities and competences as well as to view aspects of his personality. Nevertheless, using these platforms in the hiring process usually inundates employers with huge amounts of data that is often difficult to analyze. Moreover, being limited to the information available on social media runs the risk of excluding skilled candidates or hiring under qualified employees. Finally, accessing sensitive information on a job candidate{\textquoteright}s social media profile could be viewed as an invasion of privacy. Despite all these pitfalls, we strongly believe that social media represent a significant component of the hiring process that will give businesses a competitive advantage.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029761283\&partnerID=40\&md5=099a91a3a97bbda87b2b604b24ae305c},
	author = {Faizi, R.}
}
@article {Boudad2017,
	title = {Sentiment analysis in Arabic: A review of the literature},
	journal = {Ain Shams Engineering Journal},
	year = {2017},
	note = {cited By 0; Article in Press},
	abstract = {Within the last couple of years, Sentiment Analysis in Arabic has gained a considerable interest from the research community. In this respect, the objective of this paper is to provide a review of the major works that have dealt with this research area in this language. A thorough investigation of the available literature revealed that the works were mainly concentrated on dealing with specific Sentiment Analysis tasks. To this end, they used three different approaches, namely supervised, unsupervised and hybrid. The results that these studies achieved are interesting but divergent. This divergence is relatively due to the type of approach opted for, the task that is being analysed as well as to the specificities and intricacies of the Arabic variety understudy. {\textcopyright} 2017 Ain Shams University.},
	doi = {10.1016/j.asej.2017.04.007},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85025446495\&doi=10.1016\%2fj.asej.2017.04.007\&partnerID=40\&md5=4c3f2d43d536d6eec5f42878492ea75c},
	author = {Boudad, N. and Faizi, R. and Oulad Haj Thami, R. and Chiheb, R.}
}
@article {Boudad2017233,
	title = {Sentiment classification of Arabic tweets: A supervised approach},
	journal = {Journal of Mobile Multimedia},
	volume = {13},
	number = {3-4},
	year = {2017},
	note = {cited By 0},
	pages = {233-243},
	abstract = {Social media platforms have proven to be a powerful source of opinion sharing. Thus, mining and analyzing these opinions has an important role in decision-making and product benchmarking. However, the manual processing of the huge amount of content that these web-based applications host is an arduous task. This has led to the emergence of a new field of research known as Sentiment Analysis. In this respect, our objective in this work is to investigate sentiment classification in Arabic tweets using machine learning. Three classifiers namely Na{\"I}ve Bayes, Support Vector Machine and K-Nearest Neighbor were evaluated on an in-house developed dataset using different features. A comparison of these classifiers has revealed that Support Vector Machine outperforms others classifiers and achieves a 78\% accuracy rate. {\textcopyright} Rinton Press.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040241035\&partnerID=40\&md5=76bc92b38d241173585a11bc74ae14d7},
	author = {Boudad, N. and Faizi, R. and Thami, R.O.H. and Chiheb, R.}
}
@article {Faizi201532,
	title = {Students{\textquoteright} perceptions towards using Web 2.0 technologies in education},
	journal = {International Journal of Emerging Technologies in Learning},
	volume = {10},
	number = {6},
	year = {2015},
	note = {cited By 0},
	pages = {32-36},
	abstract = {The purpose of this paper is to evaluate the usefulness of Web 2.0 technologies in education. For this purpose, a survey was carried out to explore the students{\textquoteright} perceptions towards using these tools for learning purposes. Results of the research study revealed that all the respondents are greatly immersed in these social platforms and use them for many reasons. However, it was found out that almost half of the surveyed students (i.e. 47\%) devote more than 40\% of the time they spend on Web 2.0 technologies to enhance their learning in different subjects. Taking these findings into consideration, we can stipulate that Web 2.0 applications present many educational advantages for students, hence, contribute in providing opportunities for further learning. Consequently, these online tools provide schools and universities with more opportunities to go beyond traditional delivery formats and develop learnercentered personalized learning environments.},
	doi = {10.3991/ijet.v10i6.4858},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952788796\&doi=10.3991\%2fijet.v10i6.4858\&partnerID=40\&md5=c22befa5ff5f3e9fcbbfe17e466aeee9},
	author = {Faizi, R. and Chiheb, R. and El Afia, A.}
}
@article {Faizi201464,
	title = {Social media: An optimal virtual environment for learning foreign languages},
	journal = {International Journal of Emerging Technologies in Learning},
	volume = {9},
	number = {5},
	year = {2014},
	note = {cited By 3},
	pages = {64-66},
	abstract = {The present paper aims at exploring the potential role that social media technologies play in learning foreign languages. For this purpose, a survey was carried out to examine students{\textquoteright} and language learners{\textquoteright} perceptions and attitudes about using these platforms. Results of the research study revealed that the great majority of the respondents actually use these web-based applications to enhance their language skills. Most importantly, they noted that social media contribute in improving their listening, reading, speaking and writing skills. Accordingly, we strongly recommend that instructors use these online tools in distant, blended, or face-to-face language learning settings.},
	doi = {10.3991/ijet.v9i5.3911},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907689668\&doi=10.3991\%2fijet.v9i5.3911\&partnerID=40\&md5=22bc806cdbaebe7f28f0b9664aa83c8f},
	author = {Faizi, R. and El Afia, A. and Chiheb, R.}
}
