@conference { ISI:000381172301147,
	title = {An Example of a Big Data Solution Implementation},
	booktitle = {INNOVATION MANAGEMENT AND EDUCATION EXCELLENCE VISION 2020: FROM REGIONAL DEVELOPMENT SUSTAINABILITY TO GLOBAL ECONOMIC GROWTH, VOLS I - VI},
	year = {2016},
	note = {27th International Business Information Management Association Conference, Milan, ITALY, MAY 04-05, 2016},
	pages = {3263-3265},
	abstract = {Over the last decade, digital data has known an incredible growth. Several businesses have taken advantage of this data abundance, and have moved their decision making strategy from hypothesis driven to data driven approaches. Therefore, new solutions are needed to store and access this big data, then infer knowledge out of it and make it available to managers. In this paper, we will discuss briefly the different issues that should be taken into consideration for implementing a Big Data framework in companies. Then a solution implementation for a Middle East gas and oil distribution company will be presented.},
	isbn = {978-0-9860419-6-9},
	author = {Benbrahim, Houda and Khalaf, Ilias and Kassou, Ismail},
	editor = {Soliman, KS}
}
@conference { ISI:000381172301152,
	title = {Improving Recommendations Using Context from Users{\textquoteright} Reviews},
	booktitle = {INNOVATION MANAGEMENT AND EDUCATION EXCELLENCE VISION 2020: FROM REGIONAL DEVELOPMENT SUSTAINABILITY TO GLOBAL ECONOMIC GROWTH, VOLS I - VI},
	year = {2016},
	note = {27th International Business Information Management Association Conference, Milan, ITALY, MAY 04-05, 2016},
	pages = {3312-3315},
	abstract = {Recommender systems enable E-commerce websites to improve their sales by suggesting relevant items to customers. If considering the context of buying situations, more accurate recommendations can be computed. In this paper, we discuss the issue of using the contextual information contained in users{\textquoteright} reviews in order to improve recommendation.},
	isbn = {978-0-9860419-6-9},
	author = {Zahra Lahlou, Fatima and Benbrahim, Houda and Kassou, Ismail},
	editor = {Soliman, KS}
}
@conference { ISI:000381172301104,
	title = {Introduction to Competitive Intelligence: Process, Applications and Tools},
	booktitle = {INNOVATION MANAGEMENT AND EDUCATION EXCELLENCE VISION 2020: FROM REGIONAL DEVELOPMENT SUSTAINABILITY TO GLOBAL ECONOMIC GROWTH, VOLS I - VI},
	year = {2016},
	note = {27th International Business Information Management Association Conference, Milan, ITALY, MAY 04-05, 2016},
	pages = {2876-2885},
	abstract = {In the world of excessive business competitiveness, almost every company tries to monitor its environment to exceed the competitors. Getting knowledge about competitors is the basic principal of what is called Competitive Intelligence (CI). Many applications of Competitive Intelligence can be used like Opinion Mining and Foresight studies and the process of obtaining such intelligence differs according to the company{\textquoteright}s needs. In this paper, we will present in more details the definitions of CI and a general process grouping the most used steps in conducting such study. In the end we will present some tools useful in CI.},
	isbn = {978-0-9860419-6-9},
	author = {Amarouche, Kamal and El Akrouchi, Manal and Benbrahim, Houda and Kassou, Ismail},
	editor = {Soliman, KS}
}
@conference { ISI:000360508700103,
	title = {Early Warning Signs Detection in Competitive Intelligence},
	booktitle = {INNOVATION VISION 2020: FROM REGIONAL DEVELOPMENT SUSTAINABILITY TO GLOBAL ECONOMIC GROWTH, VOL I-VI},
	year = {2015},
	note = {25th International-Business-Information-Management-Association Conference, Amsterdam, NETHERLANDS, MAY 07-08, 2015},
	pages = {1014-1024},
	publisher = {Int Business Informat Management Assoc},
	organization = {Int Business Informat Management Assoc},
	abstract = {In a world of excessive competitiveness in the business environment, the competitive intelligence activities are widely used to monitor the competitors. Many techniques are used like opinion mining, event detection and foresight studies. Foresight allows studying the future and tries to predict and anticipate future actions. We can find diverse concepts and methodologies with numerous challenging research topics: trends, weak signals, wild cards... This paper will focus on weak signal detection in which we try to find the early warning signs in a performed quantitative and automatic way. It will present a literature review regarding the weak signals analysis terminology along with the processes to detect these signals, techniques, methods and approaches used.},
	isbn = {978-0-9860419-4-5},
	author = {El Akrouchi, Manal and Benbrahim, Houda and Kassou, Ismail},
	editor = {Soliman, KS}
}
@conference { ISI:000373736100045,
	title = {Product Opinion Mining for Competitive Intelligence},
	booktitle = {INTERNATIONAL CONFERENCE ON ADVANCED WIRELESS INFORMATION AND COMMUNICATION TECHNOLOGIES (AWICT 2015)},
	series = {Procedia Computer Science},
	volume = {73},
	year = {2015},
	note = {International Conference on Advanced Wireless Information and Communication Technologies (AWICT), Natl Sch Engineers Sousse, TUNISIA, OCT 05-07, 2015},
	pages = {358-365},
	abstract = {Competitive Intelligence is one of the keys of companies Risk Management. It provides the company with a permanent lighting to its competitive environment. The increasingly frequent use of Information and Communication Technologies (ICT); including (namely) online shopping sites, blogs, social network sites, forums, provides incentives for companies check their advantages over their competitors. This information presents a new source that helps and leads the company to identify, analyze and manage the various risks associated with its business/products. Nowadays, a good use of these data helps the company to improve its products/services. In this paper, an overview of opinion mining for competitive intelligence will be presented. We{\textquoteright}ll try to synthesize the major research done for the different steps of product opinion mining. (C) 2015 The Authors. Published by Elsevier B.V.},
	issn = {1877-0509},
	doi = {10.1016/j.procs.2015.12.004},
	author = {Amarouche, Kamal and Benbrahim, Houda and Kassou, Ismail},
	editor = {Boubiche, DE and Hidoussi, F and Cruz, HT}
}
@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: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}
}
