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Abdelghani Bakhtouchi

Bio: Abdelghani Bakhtouchi is an academic researcher from École Normale Supérieure. The author has contributed to research in topics: Ontology (information science) & Data integration. The author has an hindex of 4, co-authored 9 publications receiving 46 citations.

Papers
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Proceedings ArticleDOI
25 Apr 2018
TL;DR: A new syntax-based aspect detection approach for sentiment analysis in Arabic reviews based on a five-step process, namely: pre-processing, lexical entities disambiguation, separation between aspects, extraction of aspects and grouping of aspects.
Abstract: With the explosive growth of different form of social media on the Web, individuals and organizations are increasingly trying to use the content in these media for decision making. Thanks to the automatic and accurate measurement of customer opinions, the organization can integrate, understand and analyze the needs of its targets and thus adapt its marketing strategy. In this paper, we describe a new syntax-based aspect detection approach for sentiment analysis in Arabic reviews. Many characteristics of the Arabic subjective text were studied and used in order to elaborate syntactic rules and disambiguation techniques that have been integrated in our approach. The proposed approach is based on a five-step process, namely: pre-processing, lexical entities disambiguation, separation between aspects, extraction of aspects and grouping of aspects. To demonstrate the effectiveness of our proposal, we used two datasets of Arabic social content related to hotel and product reviews. The former is based on content extracted from TripAdvisor while the latter is from the Souq website. The Experimental results showed that the syntax-based approach obtains an F-measure of 69,56% (resp. 68.29%) for the hotels dataset (resp. the products dataset).

14 citations

Journal ArticleDOI
TL;DR: A methodology integrating sources referencing shared domain ontology enriched with functional dependencies (FD) that gives more autonomy to sources when choosing their primary keys and allows deriving a reconciliation key for a given query.
Abstract: Providing automatic integration solutions is the key to the success of applications managing massive amounts of data. Two main problems stand out in the major studies: (i) the management of the source heterogeneity; (ii) the reconciliation of query results. To tackle the first problem, formal ontologies are used to explicit the semantic of data. The reconciliation problem consists in deciding whether different identifiers refer to the same instance. Two main trends emerge in the reconciliation process: (i) the assumption that different source entities representing the same concept have the same key – a strong hypothesis that violates the autonomy of sources; (ii) The use of statistical methods that identify affinities between concepts – not suitable for sensitive applications. In this paper, we propose a methodology integrating sources referencing shared domain ontology enriched with functional dependencies (FD). The presence of FD gives more autonomy to sources when choosing their primary keys and allows deriving a reconciliation key for a given query. The methodology is then validated using LUBM.

9 citations

Journal ArticleDOI
TL;DR: This paper presents, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion, and defines first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies.

8 citations

Book ChapterDOI
31 Oct 2011
TL;DR: A methodology integrating sources referencing shared domain ontology enriched with functional dependencies (FD) in a mediation architecture that gives more autonomy of sources in choosing their primary keys and facilitates the result reconciliation.
Abstract: Integrating data sources is the key success of business intelligence systems. The exponential growth of autonomous data sources over the Internet and enterprise intranets makes the development of integration solutions more complex. This is due to two main factors: (i) the management of the source heterogeneity and (ii) the reconciliation of query results. To deal with the first factor, several research efforts proposed the use of ontologies to explicit semantic of each source. Two main trends are used to reconcile the query results: (i) the supposition that different entities of sources representing the same concept have the same key - a strong hypothesis that violates the autonomy of sources. (ii) The use of statistical methods which are not usually suitable for sensitive-applications. In this paper, we propose a methodology integrating sources referencing shared domain ontology enriched with functional dependencies (FD) in a mediation architecture. The presence of FD gives more autonomy of sources in choosing their primary keys and facilitates the result reconciliation. Our methodology is validated using dataset of Lehigh University Benchmark.

7 citations

Book ChapterDOI
01 Jan 2012
TL;DR: This chapter proposes an integration methodology for sources referencing shared domain ontology (called ontology-based database sources) with mediation architecture that is validated using a set of ontology based database sources in Postgres DBMS, where all mediator components are formally described.
Abstract: The exponential growth of data sources over the Internet or in enterprise intranets requires the development of data integration methodologies and solutions to facilitate data access by offering uniform interface to end users. Data integration is facing two challenges: (1) management of source heterogeneity and (2) consolidation of the query results. To deal with the problem of heterogeneity, several research efforts proposed the use of ontologies to explicit semantic of sources. This explicitation of source semantic facilitates the resolution of different conflicts identified during the integration process. Once an integration system is built (using mediator architecture) it shall support user queries, by first identifying the relevant sources for a given query and then conciliating the result. To accomplish this task, two trends emerge in the current work: (1) the supposition that different entities of sources representing the same concept have the same key. This hypothesis is not always true in real applications due to the autonomy of sources. (2) The use of statistical methods to identify similar instances. For some applications like banking and engineering, precise integration solutions are needed. In this chapter, we propose an integration methodology for sources referencing shared domain ontology (called ontology-based database sources) with mediation architecture. Our ontology is enriched by functional dependencies defined in each ontology class. The presence of these functional dependencies allows the generation of the lists of candidate keys for each class. Therefore, each source can choose its keys from these lists. This gives more autonomy of sources and allows consolidation of the results in the absence of a common identifier. Our approach is validated using a set of ontology based database sources in Postgres DBMS, where all mediator components are formally described.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis and presents a taxonomy of sentiment analysis, which highlights the power of deep learning architectures for solving sentiment analysis problems.
Abstract: Social media is a powerful source of communication among people to share their sentiments in the form of opinions and views about any topic or article, which results in an enormous amount of unstructured information. Business organizations need to process and study these sentiments to investigate data and to gain business insights. Hence, to analyze these sentiments, various machine learning, and natural language processing-based approaches have been used in the past. However, deep learning-based methods are becoming very popular due to their high performance in recent times. This paper provides a detailed survey of popular deep learning models that are increasingly applied in sentiment analysis. We present a taxonomy of sentiment analysis and discuss the implications of popular deep learning architectures. The key contributions of various researchers are highlighted with the prime focus on deep learning approaches. The crucial sentiment analysis tasks are presented, and multiple languages are identified on which sentiment analysis is done. The survey also summarizes the popular datasets, key features of the datasets, deep learning model applied on them, accuracy obtained from them, and the comparison of various deep learning models. The primary purpose of this survey is to highlight the power of deep learning architectures for solving sentiment analysis problems.

385 citations

[...]

21 May 2013
TL;DR: In this paper, a video montre un exemple de realisation d'application developpee dans l'UE GLIHM du Master Informatique, specialite E-services.
Abstract: Cette video montre un exemple de realisation d'application developpee dans l'UE GLIHM du Master Informatique, specialite E-services. L'application 'I'm in' permet de creer et de partager des evenements sur le campus de Lille 1. Chaque utilisateur peut definir des centres d’interets ce qui permet d’…

252 citations

Journal ArticleDOI
TL;DR: This survey presents a comprehensive overview of the works done so far on Arabic SA and tries to identify the gaps in the current literature laying foundation for future studies in this field.
Abstract: Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning It is concerned with the automatic extraction of opinions conveyed in a certain text Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA) The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field

153 citations

Journal ArticleDOI
TL;DR: The convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW) is described, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.
Abstract: This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), on-line analytical processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP . When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

144 citations

Journal ArticleDOI
07 May 2019
TL;DR: This article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining.
Abstract: Opinion-mining or sentiment analysis continues to gain interest in industry and academics. While there has been significant progress in developing models for sentiment analysis, the field remains an active area of research for many languages across the world, and in particular for the Arabic language, which is the fifth most-spoken language and has become the fourth most-used language on the Internet. With the flurry of research activity in Arabic opinion mining, several researchers have provided surveys to capture advances in the field. While these surveys capture a wealth of important progress in the field, the fast pace of advances in machine learning and natural language processing (NLP) necessitates a continuous need for a more up-to-date literature survey. The aim of this article is to provide a comprehensive literature survey for state-of-the-art advances in Arabic opinion mining. The survey goes beyond surveying previous works that were primarily focused on classification models. Instead, this article provides a comprehensive system perspective by covering advances in different aspects of an opinion-mining system, including advances in NLP software tools, lexical sentiment and corpora resources, classification models, and applications of opinion mining. It also presents future directions for opinion mining in Arabic. The survey also covers latest advances in the field, including deep learning advances in Arabic Opinion Mining. The article provides state-of-the-art information to help new or established researchers in the field as well as industry developers who aim to deploy an operational complete opinion-mining system. Key insights are captured at the end of each section for particular aspects of the opinion-mining system giving the reader a choice of focusing on particular aspects of interest.

76 citations