@article {Mountassir201664,
	title = {New under-sampling methods to address the problem of unbalanced sentiment classification: Application on Arabic datasets},
	journal = {International Journal of Information and Communication Technology},
	volume = {9},
	number = {1},
	year = {2016},
	note = {cited By 0},
	pages = {64-77},
	abstract = {This paper presents the study we have carried out to address the problem of unbalanced datasets in supervised sentiment classification in an Arabic context. We propose three different methods to under-sample the majority class documents. Our goal is to compare the effectiveness of the proposed methods with the common random under-sampling. We also aim to evaluate the behaviour of the classifier toward different under-sampling rates. We use three different common classifiers, namely Na{\"\i}ve Bayes, support vector machines and k-nearest neighbours. The experiments are carried out on two different Arabic datasets that we have built internally. We show that results obtained on the first dataset, which is slightly skewed, are better than those obtained on the second one which is highly skewed. We conclude also that Na{\"\i}ve Bayes is sensitive to dataset size, the more we reduce the data the more the results degrade. However, support vector machines are highly sensitive to unbalanced datasets. We record an instable behaviour of k-nearest neighbour. The results show also that we can rely on the proposed techniques and that they are typically competitive with random under-sampling. {\textcopyright} 2016 Inderscience Enterprises Ltd.},
	doi = {10.1504/IJICT.2016.077687},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979641121\&doi=10.1504\%2fIJICT.2016.077687\&partnerID=40\&md5=3261431f79a2a1c6ae94fa16e8ad8b01},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir2014442,
	title = {The nearest centroid based on vector norms: A new classification algorithm for a new document representation model},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8556 LNAI},
	year = {2014},
	note = {cited By 1},
	pages = {442-456},
	abstract = {In this paper, we present a novel model that we propose for document representation. In contrast with the classical Vector Space Model which represents each document by a unique vector in the feature space, our model consists in representing each document by a vector in the space of training documents of each category. We develop, for this novel model, a discriminative classifier which is based on the norms of the generated vectors by our model. We call this algorithm the Nearest Cetroid based on Vector Norms. Our major goal, by the proposition of such new classification framework, is to overcome the problems related to huge dimensionality and vector sparsity which are commonly faced in Text Classification problems. We evaluate the performance of the proposed framework by comparing its effectiveness and efficiency with those of some standard classifiers when used with the classical document representation. The studied classifiers are Na{\"\i}ve Bayes (NB), Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We conduct our experiments on multi-lingual balanced and unbalanced binary data sets. Our results show that our algorithm typically performs well since it is competitive with the classical methods and, at the same time, dramatically faster especially in comparison with NB and kNN. We also apply our model on the Reuters21578 corpus so as to evaluate its performance in a multi-class environment. We can say that the obtained result (85.4\% in terms of micro-F1) is promising and that it can be improved in future works. {\textcopyright} 2014 Springer International Publishing Switzerland.},
	doi = {10.1007/978-3-319-08979-9_34},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958526350\&doi=10.1007\%2f978-3-319-08979-9_34\&partnerID=40\&md5=a5d3d5cd03b740999c7e4bcf28e33e1a},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir201330,
	title = {Representing text documents in training document spaces: A novel model for document representation},
	journal = {Journal of Theoretical and Applied Information Technology},
	volume = {56},
	number = {1},
	year = {2013},
	note = {cited By 0},
	pages = {30-39},
	abstract = {In this paper, we propose a novel model for Document Representation in an attempt to address the problem of huge dimensionality and vector sparseness that are commonly faced in Text Classification tasks. The proposed model consists of representing text documents in the space of training documents at a first stage. Afterward, the generated vectors are projected in a new space where the number of dimensions corresponds to the number of categories. To evaluate the effectiveness of our model, we focus on a problem of binary classification. We conduct our experiments on Arabic and English data sets of Opinion Mining. We use as classifiers Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) which are known by their effectiveness in classical Text Classification tasks. We compare the performance of our model with that of the classical Vector Space Model (VSM) by the consideration of three evaluative criteria, namely dimensionality of the generated vectors, time (of learning and testing) taken by the classifiers, and classification results in terms of accuracy. Our experiments show that the effectiveness of our model (in comparison with the classical VSM) depends on the used classifier. Results yielded by k-NN when applying our model are better or as those obtained when applying the classical VSM. For SVM, results yielded when applying our model are in general, slightly lower than those obtained when using VSM. However, the gain in terms of time and dimensionality reduction is so promising since they are dramatically decreased by the application of our model. {\textcopyright} 2005 - 2013 JATIT \& LLS. All rights reserved.},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885352218\&partnerID=40\&md5=21216dd814d8bc9aa5e4bfb7a08e6be8},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Mountassir201373,
	title = {Sentiment classification on arabic corpora: A preliminary cross-study},
	journal = {Document Numerique},
	volume = {16},
	number = {1},
	year = {2013},
	note = {cited By 2},
	pages = {73-96},
	abstract = {The rise of social media (such as online web forums and social networking sites) has attracted interests to mining and analyzing opinions available on the web. The online opinion has become the object of studies in many research areas; especially that called "Opinion Mining and Sentiment Analysis". Several interesting and advanced works were performed on few languages (in particular English). However, there were very few studies on Morphologically Rich Languages such as Arabic. This paper presents the study we have carried out to investigate supervised sentiment classification in an Arabic context. We use two Arabic Corpora which are different in many aspects. We use three common classifiers known by their effectiveness, namely Na{\"\i}ve Bayes, Support Vector Machines and k-Nearest Neighbor. We investigate some settings to identify those that allow achieving the best results. These settings are about stemming type, term frequency thresholding, term weighting and ngram words. We show that Na{\"\i}ve Bayes and Support Vector Machines are competitively effective; however k-Nearest Neighbor{\textquoteright}s effectiveness depends on the corpus. Through this study, we recommend to use light-stemming rather than stemming, to remove terms that occur once, to combine unigram and bigram words and to use presence-based weighting rather than frequency-based one. Our results show also that classification performance can be influenced by documents length, documents homogeneity and the nature of document authors. However, the size of data sets does not have an impact on classification results. {\textcopyright} 2013 Lavoisier.},
	doi = {10.3166/DN.16.1.73-96},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879022187\&doi=10.3166\%2fDN.16.1.73-96\&partnerID=40\&md5=203c5325261be58119451ab15e8141b5},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir2012306,
	title = {Addressing the problem of unbalanced data sets in sentiment analysis},
	booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},
	year = {2012},
	note = {cited By 0},
	pages = {306-311},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881521979\&partnerID=40\&md5=c7a7678cb1231468ccf73bfed1974063},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir2012259,
	title = {A cross-study of sentiment classification on arabic corpora},
	booktitle = {Res. and Dev. in Intelligent Syst. XXIX: Incorporating Applications and Innovations in Intel. Sys. XX - AI 2012, 32nd SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.},
	year = {2012},
	note = {cited By 5},
	pages = {259-272},
	doi = {10.1007/978-1-4471-4739-8-21},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881507047\&doi=10.1007\%2f978-1-4471-4739-8-21\&partnerID=40\&md5=558cbf80cf3c4c368bae6571c00c9274},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@conference {Mountassir20123298,
	title = {An empirical study to address the problem of unbalanced data sets in sentiment classification},
	booktitle = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
	year = {2012},
	note = {cited By 7},
	pages = {3298-3303},
	doi = {10.1109/ICSMC.2012.6378300},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872383401\&doi=10.1109\%2fICSMC.2012.6378300\&partnerID=40\&md5=7e29a74d448b1a5da8a64fa5e3a0063b},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Hatim201255,
	title = {Enforcing access control in workflow systems with a task engineering approach},
	journal = {International Journal of Internet Technology and Secured Transactions},
	volume = {4},
	number = {1},
	year = {2012},
	note = {cited By 0},
	pages = {55-70},
	doi = {10.1504/IJITST.2012.045150},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878790425\&doi=10.1504\%2fIJITST.2012.045150\&partnerID=40\&md5=102c11f59819a6eff35467ade49c1d55},
	author = {Hatim, H. and Bakkali, H.E. and Berrada, I.}
}
@conference {Mountassir201243,
	title = {Some methods to address the problem of unbalanced sentiment classification in an arabic context},
	booktitle = {CiSt 2012 - Proceedings: 2012 Colloquium in Information Science and Technology},
	year = {2012},
	note = {cited By 2},
	pages = {43-48},
	doi = {10.1109/CIST.2012.6388061},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872535822\&doi=10.1109\%2fCIST.2012.6388061\&partnerID=40\&md5=81312470e1e0a89c333acbcd558540a2},
	author = {Mountassir, A. and Benbrahim, H. and Berrada, I.}
}
@article {Haddadi201187,
	title = {Establishment and application of competitive intelligence system in mobile devices},
	journal = {Journal of Intelligence Studies in Business},
	volume = {1},
	number = {1},
	year = {2011},
	note = {cited By 3},
	pages = {87-96},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905686627\&partnerID=40\&md5=96b43d91e158a53a8db2a6ea889e14a9},
	author = {Haddadi, A.E. and Dousset, B. and Berrada, I.}
}
@conference {Haddadi2011,
	title = {Xplor Everywhere - The competitive intelligence system for mobile},
	booktitle = {International Conference on Multimedia Computing and Systems -Proceedings},
	year = {2011},
	note = {cited By 0},
	doi = {10.1109/ICMCS.2011.5945635},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961229104\&doi=10.1109\%2fICMCS.2011.5945635\&partnerID=40\&md5=855b957db3f0a95be0b4115a594521fa},
	author = {Haddadi, A.E. and Dousset, B. and Berrada, I.}
}
@article {ElBakkali2010203,
	title = {A new task engineering approach for workflow access control},
	journal = {Advances in Intelligent and Soft Computing},
	volume = {85},
	year = {2010},
	note = {cited By 1},
	pages = {203-212},
	doi = {10.1007/978-3-642-16626-6_22},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053005691\&doi=10.1007\%2f978-3-642-16626-6_22\&partnerID=40\&md5=b9d1f4d19bd722e72574f0bef90c7cff},
	author = {El Bakkali, H. and Hatim, H. and Berrada, I.}
}
@conference {Hatim2010,
	title = {Workflow access control: From role engineering to task engineering},
	booktitle = {2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010},
	year = {2010},
	note = {cited By 0},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951499002\&partnerID=40\&md5=bf2b2a62594c1dd42d3dd1bbef261de3},
	author = {Hatim, H. and El Bakkali, H. and Berrada, I.}
}
@article {Elmeziane2008113,
	title = {A new artificial immune system for the detection of abnormal behaviour},
	journal = {Studies in Computational Intelligence},
	volume = {149},
	year = {2008},
	note = {cited By 1},
	pages = {113-122},
	doi = {10.1007/978-3-540-70560-4_10},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52049089233\&doi=10.1007\%2f978-3-540-70560-4_10\&partnerID=40\&md5=d907d2de93d5249a4ec8aa418b36c4a9},
	author = {Elmeziane, R. and Berrada, I. and Kassou, I.}
}
