Jmal J.,Higher Institute of Management |
ACM International Conference Proceeding Series | Year: 2013
Since E-commerce is becoming more and more popular, the number of customer reviews raises rapidly. Opinions on the Web affect our choices and decisions. Thus, it becomes necessary to automatically process a mixture of reviews and prepare to the customer the required information in an appropriate form. In the same context, we present a new approach of feature-based opinion summarization which aims to turn the customer reviews into scores that measure the customer satisfaction for a given product and its features. These scores are between 0 and 1 and can be used for decision making and then help users in their choices. We investigated opinions extracted from nouns, adjectives, verbs and adverbs contrary to previous researches which use essentially adjectives. Experimental results show that our method performs comparably to classic feature-based summarization methods. Copyright © 2013 ACM.
Cherichi S.,LARODEC |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016
Social mediating technologies have engendered radically new ways of information and communication, particularly during events; in case of natural disaster like earthquakes tsunami and American presidential election. The growing complexity of these social mediating technologies in terms of size, number of users, and variety of bloggers relationships have generated a big data which requires innovative approaches in order to analyse, extract and detect non-obvious and popular events. This paper is based on data obtained from Twitter because of its popularity and sheer data volume. This content can be combined and processed to detect events, entities and popular moods to feed various new large-scale data-analysis applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. Taking to account all the difficulties, we propose a new event detection approach combining linguistic features and Twitter features. Finally, we present our event detection system from microblogs that aims (1) detect new events, (2) to recognize temporal markers pattern of an event, (3) and to classify important events according to thematic pertinence, author pertinence and tweet volume. © Springer International Publishing Switzerland 2016.
Elkhlifi A.,Paris-Sorbonne University |
Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23 | Year: 2010
Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system. Copyright © 2010, American Association for Artificial Intelligence (www.aaai.org ). All rights reserved.
Tlili T.,LARODEC |
Theoretical Computer Science | Year: 2015
We consider in this paper a double loading problem (DLP), an NP-hard optimization problem of extreme economic relevance in industrial areas. The problem consists in loading items into bins, then stowing bins in a set of compartments. The main objective is to minimize the number of used bins. We state the mathematical model as well as a modified binary particle swarm optimization with FFD initialization that outperforms state-of-the-art approaches carried out on a large testbed instances. © 2015 Elsevier B.V.
Benferhat S.,University of Artois |
Fuzzy Sets and Systems | Year: 2011
Many algorithms deal with non-experimental data in possibilistic networks. Most of them are direct adaptations of the probabilistic approaches. In this paper, we propose to represent another kind of data which is experimental data caused by external interventions in possibilistic networks. In particular, we present different and equivalent graphical interpretations of such manipulations using an adaptation of the 'do' operator to a possibilistic framework. We then propose an efficient algorithm to evaluate effects of non-simultaneous sequences of both experimental and non-experimental data. The main advantage of our algorithm is that it unifies treatments of the two kinds of data through the conditioning process with only a small extra-cost. © 2010 Published by Elsevier B.V. All rights reserved.
Jemal D.,LARODEC |
CEUR Workshop Proceedings | Year: 2015
With the data volume that does not stop growing and the multitude of sources that led to diversity of structures, data processing needs are changing. Although, relational DBMSs remain the main data management technology for processing structured data, but faced with the massive growth in the volume of data, despite their evolution, relational databases, which have been proven for over 40 years, have reached their limits. Several organizations and researchers turned to MapReduce framework that has found great success in analyzing and processing large amounts of data on large clusters. In this paper, we will discuss MapReduce and Relational Database Management Systems as competing paradigms, and then as completing paradigms where we propose an integration approach to optimize OLAP queries process.
Attiaoui D.,LARODEC |
Dore P.-E.,LARODEC |
Martin A.,CNRS Research on Informatics and Random Systems |
Ben Yaghlane B.,LARODEC
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012
In the theory of belief functions, distances between basic belief assignments are very important in many applications like clustering, conflict measuring, reliability estimation. In the discrete domain, many measures have been proposed, however, distance between continuous belief functions have been marginalized due to the nature of these functions. In this paper, we propose an adaptation inspired from the Jousselme's distance for continuous belief functions. © 2012 Springer-Verlag.
Elkhlifi A.,Paris-Sorbonne University |
31st INFORSID 2013 | Year: 2013
A new challenge is added to the Natural Language Processing Community; how to analyze the new documents forms resulting from the Web 2.0? We are interested in a particular kind of information which is events. Thus, we propose a generic approach to extract and analyze events from text. We propose an event extraction algorithm with a polynomial complexity O(n5). This algorithm is based on developed semantic map of events. We validate the first component of our approach by the development of the "EventEC" system. Copyright © (2013) by INFORSID.
Bouchlaghem R.,LARODEC |
Elkhelifi A.,Paris Sorbone University |
Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA | Year: 2016
We propose a machine learning approach for automatically classifying opinions of Twitter texts written in Modern Standard Arabic (MSA). Tweets are classified as either positive, negative, neutral or non-opinion. Various features for opinion classification have been used which are mainly linguistic and numeric. Our in-house collected and developed training data consists of tweets preserving their specifications such as @usermentions, #hashtags which are used as tweet-particular features. Four machine learning algorithms were applied on our dataset: Support Vector Machine (SVM), Naive Bayes (NB), J48 decision tree and Random forest. The experiments results show that SVM gives the highest F measure (72%), while the j48 classifier gives the highest precision (70,97%). Our experimental results demonstrate that tweet's specific features can significantly improve classification performance in comparison to other features combination. © 2015 IEEE.
Elkhlifi A.,Paris-Sorbonne University |
2010 ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2010 | Year: 2010
Event extraction is a significant task in information extraction. This importance increases more and more with the explosion of textual data available on the Web, the appearance of Web 2.0 and the tendency towards the Semantic Web. Thus, we propose a generic approach to extract events from text and to analyze them. We propose an event extraction algorithm with a polynomial complexity O(n5), and a new similarity measurement between events. We use this measurement to gather similar events. We also present a semantic map of events, and we validate the first component of our approach by the development of the "EventEC" system.