14 results on '"Opinion-mining"'
Number of results to display per page
Search Results
2. Extraction et mise en contexte spatial des propositions relatives au transport dans le Grand Débat National.
- Author
-
Fize, Jacques, Sautot, Lucile, Lentschat, Martin, Dujourdy, Laurence, Journaux, Ludovic, and Hilal, Mohamed
- Subjects
- *
CITIZENS , *SOCIAL movements , *CORPORA , *DWELLINGS , *RAILROADS , *RURAL hospitals , *BICYCLE stores , *BICYCLE trails - Abstract
The Great National Debate, launched by Emmanuel Macron in early 2019 to respond to the "Gilets jaunes" social movement, allowed the collection of citizens' contributions on the ecological transition via an online platform. In this article, we use the corpus constituted by these contributions to identify locations where participants are asking for the development of bicycle paths and railway facilities. For this purpose, we have created a classification model to identify answers related to the theme of transportation and proposed a method for extracting contributions that reflect the contributors' proposals. We then sought to explain the observed spatial frequency of requests in relation to information describing the area of residence (urban, peri-urban, rural) and accessibility to shops and services. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. An attention-based CNN-LSTM model for subjectivity detection in opinion-mining.
- Author
-
Sagnika, Santwana, Mishra, Bhabani Shankar Prasad, and Meher, Saroj K.
- Subjects
- *
NATURAL language processing , *CONVOLUTIONAL neural networks , *DEEP learning , *SUBJECTIVITY , *BUSINESS planning - Abstract
Opinion-mining generally refers to analyzing opinions on various topics available in the form of text. It is an essential operation of natural language processing since it enables efficient decision-making and planning for users and businesses. Opinion-mining can be made more comfortable and more effective by initially performing subjectivity detection, i.e., identifying the text as subjective or objective. An opinion-mining model can better identify the opinions present in the remaining subjective statements by removing objective statements. With this reasoning, we present an efficient subjectivity detection model for improved accuracy in Opinion-mining. The model uses a strategic combination of convolutional neural network (CNN) and long short-term memory (LSTM). CNN and LSTM are state-of-the-art deep learning models that can efficiently process textual data and identify inherent connections and patterns with varying abstraction levels. The proposed work combines the strengths of both these models in an ensemble model. Effectiveness of the model is enhanced with the incorporation of an attention network. In the present task, the sentences are represented as word embeddings that include sentiment information and part-of-speech. The proposed model is applied on two movie review datasets, and its performance is evaluated compared with state-of-the-art methods. Various performance indexes have validated the superiority of the proposed model in the opinion-mining task. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
4. A Highly Automated Recommender System Based on a Possibilistic Interpretation of a Sentiment Analysis
- Author
-
Imoussaten, Abdelhak, Duthil, Benjamin, Trousset, François, Montmain, Jacky, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, Laurent, Anne, editor, Strauss, Olivier, editor, Bouchon-Meunier, Bernadette, editor, and Yager, Ronald R., editor
- Published
- 2014
- Full Text
- View/download PDF
5. Looking for Opinion in Land-Use Planning Corpora
- Author
-
Kergosien, Eric, Lopez, Cédric, Roche, Mathieu, Teisseire, Maguelonne, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Gelbukh, Alexander, editor
- Published
- 2014
- Full Text
- View/download PDF
6. Serious Questions in Playful Questionnaires
- Author
-
Takhtamysheva, Aneta, Smeddinck, Jan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Herrlich, Marc, editor, Malaka, Rainer, editor, and Masuch, Maic, editor
- Published
- 2012
- Full Text
- View/download PDF
7. Opinion-Mining Methodology for Social Media Analytics.
- Author
-
Yoosin Kim and Seung Ryul Jeong
- Subjects
DATA mining ,SOCIAL media ,SENTIMENT analysis ,TELECOMMUNICATION systems ,DATA analysis ,FOOD marketing - Abstract
Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers' opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
8. A STUDY OF OPINION INFORMATION EXTRACTION METHOD FOR THE PARTICIPATORY INFORMATION SERVICE
- Subjects
opinion-mining ,text-mining ,crawler - Abstract
Due to an increase in SNS users, information dissemination with emotion and personal opinion continues to grow. Opinion information for products and services of the company can be a beneficial information in the process of determining the future direction. Service that automatically collects the opinion information on the Internet is required. In this study, by analyzing the natural language processing and gathering information opinion, we visualize the evaluation and realistic reputation of specific things.
- Published
- 2014
9. System Presenting Primary Opinions Extracted from Blogs about the News Article Being Browsed
- Author
-
Daisuke, SATO, Hiroyuki, NAKAGAWA, Yasuyuki, TAHARA, and Akihiko , OHSUGA
- Subjects
ブログ ,意見抽出 ,opinion-mining ,形態素解析 ,morphological analysis ,weblog - Abstract
本論文では，閲覧中のWeb上のニュース記事に対する意見を個人のブログから収集し，その本文中の主張部分を抽出して提示するシステムの提案を行う．現在ニュースサイトにコメント欄が用意されているところは少なく，検索エンジンを用いても個人の意見のみを収集するのは容易ではない．そこで個人の意見を述べやすい場であるブログに着目してニュース記事に関連した意見を集め，主張を抽出する．本研究では主張とは意見の中で筆者が強く述べている主観的な部分を指す．開発中の主張提示システムの中で，本論文では主張抽出に焦点を当てる．主張抽出には人手により主張であるとされた文章から形態素解析を利用して特徴的な抽出ルールを設定した．本システムによりユーザはニュースサイトを閲覧すると同時に意見の多角的な見方が可能になり，より深い洞察が得られるようになる．評価実験において人手による正解との適合率を求めたところ70.0%となった．, In this paper, we propose a system presenting primary opinions of blogs about the news article being browsed. Today, it is not easy to find comments for news in the Internet, because most news sites have no commenting systems. Therefore, our system mines opinions from personal blogs with various viewpoints. The proposed system searches such blogs and extracts the opinions from them. In this paper, we focus on the latter functionality. Extracting opinions is based on rules to identify the characteristic phrases in the sentences considered as primary opinions in the sample data. Precision of the system output is 70.0%.
- Published
- 2011
10. Looking for Opinion in Land-Use Planning Corpora
- Author
-
Mathieu Roche, Cédric Lopez, Maguelonne Teisseire, Eric Kergosien, ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), VISEO, Numev (Labex), Geosud (Equipex), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), and Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other ,Computer science ,land use planning ,02 engineering and technology ,Fouille de données ,computer.software_genre ,Corpus ,Aménagement du territoire ,050105 experimental psychology ,Base de connaissances ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,Relevance (information retrieval) ,Land-use planning ,Opinion-mining ,Lexique ,05 social sciences ,Sentiment analysis ,000 - Autres thèmes ,Text-Mining ,Méthode ,Data science ,[INFO.INFO-TT]Computer Science [cs]/Document and Text Processing ,C30 - Documentation et information ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,lexicon ,020201 artificial intelligence & image processing ,knowledge base ,Data mining ,P01 - Conservation de la nature et ressources foncières ,U30 - Méthodes de recherche ,computer - Abstract
International audience; A great deal of research on opinion mining and sentiment analysis has been done in specific contexts such as movie reviews, commercial evaluations, campaign speeches, etc. In this paper, we raise the issue of how appropriate these methods are for documents related to land-use planning. After highlighting limitations of existing proposals and discussing issues related to textual data, we present the method called Opiland (OPinion mIning from LAND-use planning documents) designed to semi-automatically mine opinions in specialized contexts. Experiments are conducted on a land-use planning dataset, and on three datasets related to others areas highlighting the relevance of our proposal.
- Published
- 2014
- Full Text
- View/download PDF
11. From knowledge extraction to recommender system
- Author
-
Duthil, Benjamin, Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Ecole Nationale Supérieure des Mines d'Alès, Jacky Montmain, Pascal Poncelet, and CHABANNE, Cécile
- Subjects
multicriteria analysis ,Fouille de données ,Extraction d'opinion ,Text-mining ,Système de recommandation ,Concept characterization ,Extraction conceptuelle ,analyse multicritère ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,Fouille de texte ,[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR] ,Data-mining ,Opinion-mining ,Recommender system - Abstract
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.
- Published
- 2012
12. De l'extraction des connaissances à la recommandation
- Author
-
Duthil, Benjamin, Laboratoire de Génie Informatique et Ingénierie de Production (LGI2P), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Ecole Nationale Supérieure des Mines d'Alès, and Jacky Montmain, Pascal Poncelet
- Subjects
Concept characterization ,multicriteria analysis ,Extraction conceptuelle ,analyse multicritère ,[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] ,Fouille de texte ,Data-mining ,Fouille de données ,Extraction d'opinion ,Opinion-mining ,Recommender system ,Text-mining ,Système de recommandation - Abstract
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.
- Published
- 2012
13. Ethical, Legal, and Social Issues (ELSI)
- Author
-
Jerkovic´, Andrea, Siedschlag, Alexander, Jerkovic´, Andrea, and Siedschlag, Alexander
14. Leistungsfördernde und Zukunftsorientierte Mitarbeiterführung im Hotelgewerbe
- Author
-
Sharov, Roman
- Subjects
Work-Life-Balance ,Weiterbildung ,Hotel-business ,Arbeitsbedingungen ,Hotelgewerbe ,Education ,working-conditions ,superior behavior ,Leadership ,Vorgesetztenverhalten ,Employer-motivation ,Hotel-industry ,Mitarbeitermotivation ,Incentives ,Anreize ,Opinion-Mining ,Führung ,österreichische Hotellerie - Abstract
Die hohe Mitarbeiterfluktuation und Heterogenität des Personals in den meisten klein- und mittelständischen (KM) Hotels stellen ein zwingendes Problem für die Entwicklung und Produktivität des Gastgewerbes in Österreich dar. In wirtschaftlich schwierigen und turbulenten Zeiten des österreichischen Tourismus, wie jetzt, werden dringend neue, effiziente Führungspraktiken und Managementinstrumente benötigt, um Hotelmitarbeiter besser zu motivieren und eine weitere Zunahme der freiwilligen Fluktuation des Hotelpersonals zu verhindern.Die vorliegende Masterarbeit beschäftigt sich daher mit der Erarbeitung und Validierung neuer Managementinstrumente und -praktiken zur Motivation des Hotelpersonals, zu höherer Produktivität und geringerer Personalfluktuation. Im Fokus dieser analytischen Fallstudie stand die Kausalanalyse der Mitarbeiterfluktuation und die gezielte Untersuchung direkter Zusammenhänge zwischen dem praktizierten Führungsstil von Hoteliers und der Personalausfallrate. Die zentrale Forschungsfrage wurde wie folgt formuliert: Welcher Zusammenhang besteht zwischen Mitarbeitermotivation, Fluktuationsrate und dem Stil der Personalführung in der Hotellerie?Dementsprechend gibt die Arbeit eine kurze Forschungsgeschichte der gestellten Fragestellung und gibt einen kritischen Überblick über bestehende Motivationspraktiken in Hotels. Der direkte Zusammenhang zwischen Mitarbeitermotivation, Leistung und freiwilliger Fluktuation wurde hervorgehoben. Anschließend wurden die dafür am besten geeigneten Führungsstile, Strategien und Managementinstrumente zur Lösung von Beschäftigungsproblemen in der Hotellerie identifiziert und diskutiert. Das Konzept des zukunftsorientierten Managements mit geeigneten Instrumenten zum produktiven Personaleinsatz wurde vorgestellt und deren Fähigkeit zur signifikanten Steigerung der Mitarbeitermotivation und -zufriedenheit in 31 österreichischen KM-Hotels mit Methoden der Datenbankrecherche, Opinion-Mining und statistischer Analyse evaluiert und nachgewiesen.Vergleichbare qualitative Daten zur Mitarbeitermotivation und -zufriedenheit in ausgewählten Hotels wurden von der österreichischen Internetplattform ‚Kununu‘ erhoben, überprüft und für die weitere quantitative Analyse neu strukturiert. Eine statistisch repräsentative Stichprobe von 517 einzelnen Hotelbewertungen aus einem 11-Jahres-Zeitraum (2010-2021) wurde aufgearbeitet und hinsichtlich zwölf grundlegender Motivationsfaktoren und Zufriedenheitskriterien analysiert. Die durchschnittliche Bewertung gewisser Faktoren wurde berechnet und alle Hotels wurden entsprechend in drei Gruppen von High-, Medium- und Low-Performern eingeteilt. Die subjektive Bedeutung bzw. Gewichtung jedes Motivationsfaktors wurde getrennt von der Gesamtzahl der relevanten Bewertungen geschätzt. Infolge wurde der Führungsstil als wichtigster Einflussfaktor für die Mitarbeiterzufriedenheit und freiwillige Fluktuation gewertet, während eine faire Mitarbeitervergütung und Sozialleistungen als zweitwichtigster von zwölf Faktoren bewertet wurde. Der dritte Platz ging an die Karrierechancen und die entsprechende Ausbildung für eine sichere Zukunft.Die jüngsten Hotelbewertungen haben die Bedeutung neuer flexibler Beschäftigungsmodelle auf der Grundlage von Teilzeit- oder Fernarbeit, Jobsharing und Rotation hervorgehoben. Interessanterweise wurde in leistungsschwachen Hotels oft der kollegiale Zusammenhalt als Hauptmotivation angegeben, während das Image des Hotels praktisch vernachlässigt wurde. Diese Ergebnisse stimmen weitgehend mit denen anderer Forscher überein, zeigen jedoch einen neuen Trend im Zusammenhang zwischen dem Stil der Hotelführung und der Motivation und Zufriedenheit der Mitarbeiter im Sinne einer engeren persönlichen Beziehung, der zu einem entsprechenden Paradigmenwechsel im Hotelmanagement führt für eine sichere Zukunft für alle., Roman Sharov, Masterarbeit Universität Klagenfurt, Universitätslehrgang Tourismusmanagement 2021, in englischer Sprache
Catalog
Books, media, physical & digital resources
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.