@conference { ISI:000345642100034,
	title = {The Nearest Centroid Based on Vector Norms: A New Classification Algorithm for a New Document Representation Model},
	booktitle = {MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION, MLDM 2014},
	series = {Lecture Notes in Artificial Intelligence},
	volume = {8556},
	year = {2014},
	note = {10th International Conference on Machine Learning and Data Mining (MLDM), St Petersburg, RUSSIA, JUL 21-24, 2014},
	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 Naive 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.},
	isbn = {978-3-319-08979-9; 978-3-319-08978-2},
	issn = {0302-9743},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham},
	editor = {Perner, P}
}
@conference { ISI:000341079600096,
	title = {Context Extraction from Reviews for Context Aware Recommendation Using Text Classification Techniques},
	booktitle = {2013 ACS INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA)},
	year = {2013},
	note = {IEEE and Arab-Computing-Society (ACS) International Conference on Computer Systems and Applications (AICCSA), Al Akhawayn Univ, Ifrane, MOROCCO, MAY 27-30, 2013},
	publisher = {IEEE; Arab Comp Soc},
	organization = {IEEE; Arab Comp Soc},
	abstract = {In this paper, we investigate the use of Text Classification techniques to extract contextual information from user reviews for Context Aware Recommendation. We conduct several experiments to identify the best Text Representation settings and the best classification algorithm for our dataset. We carry out our experiments on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Naive Bayes classifier yields the best results with up to 72.2\% in terms of F1-measure. To extract context from user reviews with text classification techniques, we recommend to use raw text rather than employing stemming, to use the normalized frequency based weighting rather than the presence based one, to remove terms that occur once in the data set, and to combine unigrams, bigrams and trigrams.},
	isbn = {978-1-4799-0792-2},
	author = {Lahlou, Fatima Zahra and Benbrahimand, Houda and Mountassir, Asmaa and Kassou, Ismail}
}
@conference { ISI:000341079600083,
	title = {A Novel Model for Document Representation},
	booktitle = {2013 ACS INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA)},
	year = {2013},
	note = {IEEE and Arab-Computing-Society (ACS) International Conference on Computer Systems and Applications (AICCSA), Al Akhawayn Univ, Ifrane, MOROCCO, MAY 27-30, 2013},
	publisher = {IEEE; Arab Comp Soc},
	organization = {IEEE; Arab Comp Soc},
	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. We conduct our experiments on data sets of Opinion Mining. We use as classifiers Support Vector Machines (SVM) and k-Nearest Neighbors (kNN). We compare the performance of our model with that of the classical representation based on Vector Space Model (VSM). Our experiments show that the effectiveness of our model depends on the used classifier. Results yielded by kNN when applying our model are the same as those obtained when applying the classical VSM. For SVM, results yielded when applying our model are typically 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.},
	isbn = {978-1-4799-0792-2},
	author = {Mountassir, Asmaa}
}
@conference { ISI:000326649800021,
	title = {A Text Classification Based Method for Context Extraction from Online Reviews},
	booktitle = {2013 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS: THEORIES AND APPLICATIONS (SITA)},
	year = {2013},
	note = {8th International Conference on Intelligent Systems - Theories and Applications (SITA), Ecole Mohammadia Ingenieurs, Rabat, MOROCCO, MAY 08-09, 2013},
	publisher = {IEEE; IEEE Morocco Sect; Univ Mohammed V Agdal},
	organization = {IEEE; IEEE Morocco Sect; Univ Mohammed V Agdal},
	abstract = {Recommender systems are systems that filter information depending on users{\textquoteright} profiles and suggest items that might match their preferences. While the majority of existing researches compute recommendation by considering only users and items, Context Aware Recommendation Systems (CARS) consider, in addition to users and items, others features related to the context. A first issue in CARS studies is to identify the contextual features. In this paper, we investigate the use of Text Classification techniques to extract contextual features from users{\textquoteright} reviews. We conduct experiments to identify the best classification algorithm for our dataset. We evaluate our approach on hotel reviews. We focus on extracting the trip type, as contextual information, from these reviews. Results show that the Multinomial Naive Bayes performs best in our dataset, with a F1 score of 60.1\%. Since contextual information are not always provided in the reviews, we think that our results are promising. We conclude that this research area needs deeper studies.},
	isbn = {978-1-4799-0299-6},
	author = {Zahra Lahlou, Fatima and Mountassir, Asmaa and Benbrahim, Houda and Kassou, Ismail}
}
@conference { ISI:000316869203071,
	title = {An empirical study to address the problem of Unbalanced Data Sets in Sentiment Classification},
	booktitle = {PROCEEDINGS 2012 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)},
	series = {IEEE International Conference on Systems Man and Cybernetics Conference Proceedings},
	year = {2012},
	note = {IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, SOUTH KOREA, OCT 14-17, 2012},
	pages = {3298-3303},
	publisher = {IEEE Systems, Man, and Cybernetics Soc (SMC); IEEE; Korea Univ; Korean Soc Cognit Sci (KSCS); Korean Inst Informat Scientists and Engineers Soc Computat Intelligence (KSCI); Hi Seoul; Korea Tourism Org; Asian Off Aerosp Res and Dev (AOARD); Natl Res Fdn K},
	organization = {IEEE Systems, Man, and Cybernetics Soc (SMC); IEEE; Korea Univ; Korean Soc Cognit Sci (KSCS); Korean Inst Informat Scientists and Engineers Soc Computat Intelligence (KSCI); Hi Seoul; Korea Tourism Org; Asian Off Aerosp Res and Dev (AOARD); Natl Res Fdn K},
	abstract = {With the emergence of Web 2.0, Sentiment Analysis is receiving more and more attention. Several interesting works were performed to address different issues in Sentiment Analysis. Nevertheless, the problem of Unbalanced Data Sets was not enough tackled within this research area. This paper presents the study we have carried out to address the problem of unbalanced data sets in supervised sentiment classification in a multi-lingual context. We propose three different methods to under-sample the majority class documents. These methods are Remove Similar, Remove Farthest and Remove by Clustering. Our goal is to compare the effectiveness of the proposed methods with the common random under-sampling. We also aim to evaluate the behavior of the classifiers toward different under-sampling rates. We use three different common classifiers, namely Naive Bayes, Support Vector Machines and k-Nearest Neighbors. The experiments are carried out on two Arabic data sets and an English data set. We show that the four under-sampling methods are typically competitive. Naive Bayes is shown as insensitive to unbalanced data sets. But Support Vector Machines seems to be highly sensitive to unbalanced data sets; k-Nearest Neighbors shows a slight sensitivity to imbalance in comparison with Support Vector Machines.},
	isbn = {978-1-4673-1714-6},
	issn = {1062-922X},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham}
}
@conference { ISI:000371495900008,
	title = {Some methods to address the problem of Unbalanced Sentiment Classification in an Arabic context},
	booktitle = {2012 COLLOQUIUM ON INFORMATION SCIENCE AND TECHNOLOGY (CIST{\textquoteright}12)},
	series = {Colloquium in Information Science and Technology},
	year = {2011},
	note = {IEEE Colloquium on Information Science and Technology (CIST), Fez, MOROCCO, OCT 22-24, 2012},
	pages = {43-48},
	publisher = {IEEE; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; USMBA IEEE Student Branch; Faculty of Sciences Dhar Mahraz; Faculty of Technical Sciences of Fez; IEEE Comp Soc; IEEE Commun Soc},
	organization = {IEEE; IEEE Morocco Sect; IEEE Morocco Comp \& Commun Joint Chapter; USMBA IEEE Student Branch; Faculty of Sciences Dhar Mahraz; Faculty of Technical Sciences of Fez; IEEE Comp Soc; IEEE Commun Soc},
	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 {\textquoteleft}{\textquoteleft}Opinion Mining and Sentiment Analysis{{\textquoteright}{\textquoteright}}. Several interesting and advanced works were performed on few languages (in particular English). However, there were very few studies on some languages such as Arabic. This paper presents the study we have carried out to address the problem of unbalanced data sets 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 behavior of the classifier toward different under-sampling rates. We use two different common classifiers, namely Naive Bayes and Support Vector Machines. The experiments are carried out on an Arabic data set that we have built from Aljazeera{\textquoteright}s web site and labeled manually. The results show that Naive Bayes is sensitive to data set size, the more we reduce the data the more the results degrade. However, it is not sensitive to unbalanced data sets on the contrary of Support Vector Machines which is highly sensitive to unbalanced data sets. The results show also that we can rely on the proposed techniques and that they are typically competitive with random under-sampling.},
	isbn = {978-1-4673-2725-1},
	issn = {2327-185X},
	author = {Mountassir, Asmaa and Benbrahim, Houda and Berrada, Ilham},
	editor = {ElMohajir, M and Begdouri, A and ElMohajir, BE and Zarghili, A}
}
