A text-mining and possibility theory based model using public reports to highlight the sustainable development strategy of a city

International audience—Nowadays, ecology and sustainable development are priority government's actions. In Europe, and more specifically in France, sustainable development (SD) is generally broken down into several distinct evaluation criteria. Each criterion is a requirement imposed by the government and corresponds to strategic stakes. When SD improvement actions are financed in an economic region or a city of the French territory by the government, a set of measures is usually set up to assess and control the impact of these actions. More precisely, these measures are used to check whether the region or the city has efficiently invested its budget in respect to the SD strategy of the government. This assessment process is a complex task for the government. Indeed, evaluations are only based on reports provided by the financed regions. These very numerous reports are written in natural language and thus, it is a thorny and time-consuming task for the government to efficiently identify the meaningful information in a plethora of reports and then objectively assess all the expected priorities. This project aims at automating the assessment process from the huge corpus of documents. Text-mining and segmentation techniques are introduced to automatically quantify the attention the region or the city pays to a given criterion. Obviously, this quantification can only be imprecisely determined. Then, the possibility theory is used to merge the information related to each criterion prioritization from all the documents. Finally, an application on the 265 largest cities in France shows the potential of the approach

How ontology based information retrieval systems may benefit from lexical text analysis

International audienceThe exponential growth of available electronic data is almost useless without efficient tools to retrieve the right information at the right time. It is now widely acknowledged that information retrieval systems need to take semantics into account to enhance the use of available information. However, there is still a gap between the amounts of relevant information that can be accessed through optimized IRSs on the one hand, and users' ability to grasp and process a handful of relevant data at once on the other. This chapter shows how conceptual and lexical approaches may be jointly used to enrich document description. After a survey on semantic based methodologies designed to efficiently retrieve and exploit information, hybrid approaches are discussed. The original approach presented here benefits from both lexical and ontological document description, and combines them in a software architecture dedicated to information retrieval and rendering in specific domains

De l'extraction des connaissances à la recommandation.

Information Technology and the success of its related services (blogs; forums; etc.) have paved the way for a massive mode of opinion expression on the most varied subjects (e-commerce websites; art reviews; etc). This abundance of opinions could appear as a real gold mine for internet users, but it can also be a source of indecision because available opinions may be ill-assorted if not contradictory. A reliable and relevant information management of opinions bases requires systems able to directly analyze the content of opinions expressed in natural language. It allows controlling subjectivity in evaluation process and avoiding smoothing effects of statistical treatments. Most of the so-called recommender systems are unable to manage all the semantic richness of a review and prefer to associate to the review an assessment system that supposes a substantial implication and specific competences of the internet user. Our aim is minimizing user intervention in the collaborative functioning of recommender systems thanks to an automated processing of available reviews in natural language by the recommender system itself. Our topic segmentation method extracts the subjects of interest from the,reviews; and then our sentiment analysis approach computes the opinion related to these criteria. These knowledge extraction methods are combined with multicriteria analysis techniques adapted to expert assessments fusion. This proposal should finally contribute to the coming of a new generation of more relevant; reliable and personalized recommender systems.Les technologies de l'information et le succès des services associés (forums, sites spécialisés, etc) ont ouvert la voie à un mode d'expression massive d'opinions sur les sujets les plus variés (e-commerce, critiques artistiques, etc). Cette profusion d'opinions constitue un véritable eldorado pour l'internaute, mais peut rapidement le conduire à une situation d'indécision car,les avis déposés peuvent être fortement disparates voire contradictoires. Pour une gestion fiable et pertinente de l'information contenue dans ces avis, il est nécessaire de mettre en place des systèmes capables de traiter directement les opinions exprimées en langage naturel afin d'en contrôler la subjectivité et de gommer les effets de lissage des traitements statistiques. La plupart des systèmes dits de recommandation ne prennent pas en compte toute la richesse sémantique des critiques et leur associent souvent des systèmes d'évaluation qui nécessitent une implication conséquente et des compétences particulières chez l'internaute. Notre objectif est de minimiser l'intervention humaine dans le fonctionnement collaboratif des systèmes de recommandation en automatisant l'exploitation des données brutes que constituent les avis en langage naturel. Notre approche non supervisée de segmentation thématique extrait les sujets d'intérêt des critiques, puis notre technique d'analyse de sentiments calcule l'opinion exprimée sur ces critères. Ces méthodes d'extraction de connaissances combinées à des outils d'analyse multicritère adaptés à la fusion d'avis d'experts ouvrent la voie à des systèmes de recommandation pertinents, fiables et personnalisés

From knowledge extraction to recommendation

Les technologies de l'information et le succès des services associés (forums, sites spécialisés, etc) ont ouvert la voie à un mode d'expression massive d'opinions sur les sujets les plus variés (e-commerce, critiques artistiques, etc). Cette profusion d'opinions constitue un véritable eldorado pour l'internaute, mais peut rapidement le conduire à une situation d'indécision car les avis déposés peuvent être fortement disparates voire contradictoires. Pour une gestion fiable et pertinente de l'information contenue dans ces avis, il est nécessaire de mettre en place des systèmes capables de traiter directement les opinions exprimées en langage naturel afin d'en contrôler la subjectivité et de gommer les effets de lissage des traitements statistiques. La plupart des systèmes dits de recommandation ne prennent pas en compte toute la richesse sémantique des critiques et leur associent souvent des systèmes d'évaluation qui nécessitent une implication conséquente et des compétences particulières chez l'internaute. Notre objectif est de minimiser l'intervention humaine dans le fonctionnement collaboratif des systèmes de recommandation en automatisant l'exploitation des données brutes que constituent les avis en langage naturel. Notre approche non supervisée de segmentation thématique extrait les sujets d'intérêt des critiques, puis notre technique d'analyse de sentiments calcule l'opinion exprimée sur ces critères. Ces méthodes d'extraction de connaissances combinées à des outils d'analyse multicritère adaptés à la fusion d'avis d'experts ouvrent la voie à des systèmes de recommandation pertinents, fiables et personnalisés.Information Technology and the success of its related services (blogs, forums, etc.) have paved the way for a massive mode of opinion expression on the most varied subjects (e-commerce websites, art reviews, etc). This abundance of opinions could appear as a real gold mine for internet users, but it can also be a source of indecision because available opinions may be ill-assorted if not contradictory. A reliable and relevant information management of opinions bases requires systems able to directly analyze the content of opinions expressed in natural language. It allows controlling subjectivity in evaluation process and avoiding smoothing effects of statistical treatments. Most of the so-called recommender systems are unable to manage all the semantic richness of a review and prefer to associate to the review an assessment system that supposes a substantial implication and specific competences of the internet user. Our aim is minimizing user intervention in the collaborative functioning of recommender systems thanks to an automated processing of available reviews in natural language by the recommender system itself. Our topic segmentation method extracts the subjects of interest from the reviews, and then our sentiment analysis approach computes the opinion related to these criteria. These knowledge extraction methods are combined with multicriteria analysis techniques adapted to expert assessments fusion. This proposal should finally contribute to the coming of a new generation of more relevant, reliable and personalized recommender systems

Review of the Impact of IT on the Environment and Solution with a Detailed Assessment of the Associated Gray Literature

International audienceThe immaterial aspect of digital technology tends to make us forget its growing impact on the environment. Today, the situation has changed: we are becoming aware of the material aspect of digital technology, especially with the recent datacenter fires. The topic is now on the political agenda, and for good reason: digital accounts for nearly 4% of global emissions, and emissions from the sector are growing exponentially. Digital impact analysis is crucial to increase the visibility and consequently the diffusion and democratization of responsible digital. This article offers a detailed study and solutions on the environmental impact of IT with a detailed assessment of the gray literature of the last years and accompanying elements such as LCA or ISO standards. Thus, we have a view of the main green IT tools, the environmental impact of digital on datacenters, user equipment and networks, recent forecasts, and a look at the future challenges of digital technologies (such as AI or Blockchain),and finally a conclusion with the limitations of our research

Opinion Extraction Applied to Criteria

Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is easy to get this overall assessment, a more detailed analysis will highlight that the opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach.

Extraction d'opinions appliquée à des critères

National audienceLes technologies de l'information et le succès des services associés (e.g., blogs, forums,...) ont ouvert la voie à un mode d'expression massive d'opin- ions sur les sujets les plus variés. Récemment de nouvelles techniques de détec- tion automatique d'opinions (opinion mining) ont fait leur apparition et via des analyses statistiques des avis exprimés tendent à dégager une tendance globale des opinions exprimées par les internautes. Néanmoins une analyse plus fine de celle-ci montre que les arguments avancés par les internautes relèvent de critères de jugement distincts. Ici, un film sera décrié pour un scénario décousu, là il sera encensé pour une bande son époustouflante. Dans cet article, nous pro- posons, après avoir caractérisé automatiquement des critères dans un document, d'en extraire l'opinion relative. A partir d'un ensemble restreint de mots clés d'opinions, notre approche construit automatiquement une base d'apprentissage de documents issus du web et en déduit un lexique de mots ou d'expressions d'opinions spécifiques au domaine d'application. Des expériences menées sur des jeux de données réelles illustrent l'efficacité de l'approche

Towards an Automatic Characterization of Criteria

International audienceThe number of documents is growing exponentially with the rapid expansion of the Web. The new challenge for Internet users is now to rapidly find appropriate data to their requests. Thus information retrieval, automatic classification and detection of opinions appear as major issues in our information society. Many efficient tools have already been proposed to Internet users to ease their search over the web and support them in their choices. Nowadays, users would like genuine decision tools that would efficiently support them when focusing on relevant information according to specific criteria in their area of interest. In this paper, we propose a new approach for automatic characterization of such criteria. We bring out that this approach is able to automatically build a relevant lexicon for each criterion. We then show how this lexicon can be useful for documents classification or segmentation tasks. Experiments have been carried out with real datasets and show the efficiency of our proposal