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Analyse automatique d’opinions : états des lieux et perspectives
Analyse automatique d’opinions : états des lieux et perspectives
Le Web est devenu une source d’information incontournable grâce à la quantité et à la diversité des contenus textuels porteurs d’opinions générés par les internautes.Ces contenus sont multiples : blogs, commentaires, forums, réseaux sociaux, etc. Devant cette abondance de données, le développement d’outils pour extraire, synthétiser et comparer les opinions exprimées sur un sujet donné devient crucial. Cet article dresse un panorama des principales approches en analyse automatique d’opinions. Trois questions fondamentales sont abordées : comment reconnaître les portions de textes qui renseignent l’utilisateur sur l’opinion qu’il recherche ? Comment évaluer la polarité des opinions qui en ressortent ? Comment présenter le résultat de manière pertinente à l’utilisateur ?
The expression of opinion is a central aspect of user-generated contents on the Web. It enables us to convey feelings, assessments of people, situations and objects, and to engage with other opinion holders. These contents may take various forms: blogs,fora, reviews, social media, etc. To deal with the variety and volume of these data,speci?c tools have to be designed to extract, summarize and compare opinions expressed on a given subject. This article surveys the main approaches in analysisfocusing on three main questions: How can systems identify subjective spans in texts? How can they calculate the positivity or negativity of such spans? How can they accurately present the extracted opinions to end users?
- Université Paris Diderot France
natural language processing, opinion mining, informationextraction, machine learning, Traitement automatique deslangues, analyse d'opinions, extraction d'information, apprentissage automatique, [INFO] Computer Science [cs], [INFO]Computer Science [cs]
natural language processing, opinion mining, informationextraction, machine learning, Traitement automatique deslangues, analyse d'opinions, extraction d'information, apprentissage automatique, [INFO] Computer Science [cs], [INFO]Computer Science [cs]
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
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- Université Paris Diderot France
Le Web est devenu une source d’information incontournable grâce à la quantité et à la diversité des contenus textuels porteurs d’opinions générés par les internautes.Ces contenus sont multiples : blogs, commentaires, forums, réseaux sociaux, etc. Devant cette abondance de données, le développement d’outils pour extraire, synthétiser et comparer les opinions exprimées sur un sujet donné devient crucial. Cet article dresse un panorama des principales approches en analyse automatique d’opinions. Trois questions fondamentales sont abordées : comment reconnaître les portions de textes qui renseignent l’utilisateur sur l’opinion qu’il recherche ? Comment évaluer la polarité des opinions qui en ressortent ? Comment présenter le résultat de manière pertinente à l’utilisateur ?
The expression of opinion is a central aspect of user-generated contents on the Web. It enables us to convey feelings, assessments of people, situations and objects, and to engage with other opinion holders. These contents may take various forms: blogs,fora, reviews, social media, etc. To deal with the variety and volume of these data,speci?c tools have to be designed to extract, summarize and compare opinions expressed on a given subject. This article surveys the main approaches in analysisfocusing on three main questions: How can systems identify subjective spans in texts? How can they calculate the positivity or negativity of such spans? How can they accurately present the extracted opinions to end users?