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[...e.target.classList];\r\nconsole.log(targetClassesArr);\r\nif (targetClassesArr.includes('share-icon--linkedin')) {\r\nconst shareURL = `https://www.linkedin.com/sharing/share-offsite/?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--facebook')) {\r\nconst shareURL = `http://www.facebook.com/sharer/sharer.php?u=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--twitter')) {\r\nconst shareURL = `https://twitter.com/intent/tweet?url=${pageURL}`;\r\nwindow.open(shareURL, '_blank');\r\n}\r\nif (targetClassesArr.includes('share-icon--email')) {\r\nconst shareURL = `mailto:?subject=Check out this news from Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"]}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/fr/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"]}},":itemsOrder":["text","experiencefragment"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"fundamentals-main-content",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-large",":items":{"container":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"overview",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-ee81f8862b","type":"heading3","lines":["Présentation"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-1405fb0eb8","text":"\u003Cp\u003EL’analyse des opinions est une technique utilisée pour comprendre le ton émotionnel ou le sentiment — positif, négatif ou neutre — dans des données textuelles. En s’appuyant sur des techniques avancées telles que le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/natural-language-processing/\"\u003Etraitement du langage naturel (NLP)\u003C/a\u003E et le \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/deep-learning/\"\u003Edeep learning\u003C/a\u003E, ce processus classe les opinions comme positives, négatives ou neutres, offrant une façon structurée d’interpréter les données non structurées telles que les avis des clients, les publications sur les réseaux sociaux, les tickets d’assistance, les appels commerciaux ou les études et rapports du secteur. Pour les entreprises, l’analyse des opinions offre un avantage stratégique. Par exemple, avec l’augmentation du volume d’interactions avec les clients en ligne, ces outils fournissent un point de vue crucial sur la perception de la marque et les opinions des clients à grande échelle. De la même façon, la synthèse d’études d’analystes peut révéler les opinions du secteur à l’égard de produits, d’entreprises ou de technologies spécifiques. Cependant, maîtriser l’analyse des opinions implique plus qu’une seule approche. Cet article explore la mécanique de l’analyse des opinions, ses différents types, les défis inhérents, les avantages qu’elle offre et les méthodes d’évaluation de ses performances. Il est essentiel de comprendre ces aspects pour les entreprises qui cherchent à transformer les informations sur les opinions en informations exploitables pour améliorer l’expérience client et affiner leurs stratégies commerciales.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"intro",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-bb26241201","additionalClasses":"headline-decoration","type":"heading3","lines":["Introduction à l’analyse des opinions"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-5f7d5644eb","text":"\u003Cp\u003EL’analyse des opinions est une technique essentielle dans le domaine du NLP, qui aide à déterminer le ton émotionnel derrière un texte. En classant le texte comme positif, négatif ou neutre, l’analyse des opinions fournit aux entreprises des informations stratégiques sur les attitudes et les opinions de leurs clients. Ces informations sont précieuses pour prendre des décisions éclairées, améliorer les produits et les services et accroître la satisfaction des clients.\u003C/p\u003E\n\u003Cp\u003EAvec la croissance exponentielle des réseaux sociaux, les entreprises reçoivent un grand nombre de commentaires de leurs clients. L’analyse des opinions sur les réseaux sociaux permet aux entreprises de surveiller en temps réel les perceptions du public à l’égard de leur marque, de leurs produits ou de leurs campagnes. Cette fonctionnalité aide les entreprises à identifier les tendances, à évaluer les stratégies marketing et à répondre rapidement aux commentaires négatifs. Le résultat ? Amélioration de l’engagement client, renforcement de la réputation de la marque et stratégies commerciales plus réactives.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"]},"container_copy_copy":{"columnClassNames":{"title_v2_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"sentiment",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-037326fcf7","type":"heading3","lines":["Fonctionnement de l’analyse des opinions"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-739c0b8122","text":"\u003Cp\u003EL’analyse des opinions peut se faire via deux flux de travail : à l’aide d’algorithmes traditionnels basés sur le machine learning ou de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/large-language-model/\"\u003Egrands modèles de langage (LLM)\u003C/a\u003E.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy":{"id":"title-v2-a443c2292f","type":"heading4","lines":["Utilisation d’algorithmes de machine learning :"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-7b5a2763e4","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EL’analyse des opinions implique plusieurs étapes pour analyser et déterminer les opinions exprimées dans un texte. Le processus commence par le \u003Cb\u003Eprétraitement du texte\u003C/b\u003E, qui comprend le nettoyage et la préparation du texte pour analyse. Cette étape peut impliquer la suppression de la ponctuation, la conversion du texte en minuscules, la suppression des mots d’arrêt et la gestion des caractères spéciaux ou des emojis pour créer un jeu de données cohérent.\u003C/p\u003E\n\u003Cp\u003EEnsuite, des \u003Cb\u003Ealgorithmes de classification des opinions \u003C/b\u003Esont appliqués pour classer les opinions exprimées dans le texte. Ces algorithmes utilisent des techniques telles que le machine learning, le NLP et des modèles statistiques. Les approches courantes sont les suivantes :\u003C/p\u003E\n\u003Cp\u003E \u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ELes algorithmes basés sur des règles\u003C/b\u003E utilisent des règles linguistiques prédéfinies pour déterminer les opinions.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ELes algorithmes de machine learning\u003C/b\u003E apprennent à partir de jeux de données d’entraînement étiquetés pour classer les opinions.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ELes algorithmes basés sur le lexique\u003C/b\u003E s’appuient sur des dictionnaires des opinions qui attribuent des scores d’opinion aux mots ou aux phrases.\u003Cbr\u003E\n \u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E\u003Cb\u003ELa classification polaritaire\u003C/b\u003E, un sous-ensemble de l'analyse des opinions, se concentre sur la catégorisation des opinions exprimées comme positives, négatives ou neutres. Cette forme d’analyse est essentielle pour prendre des décisions plus éclairées basées sur les commentaires des clients, les tendances des réseaux sociaux et les études de marché.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138","title_v2_copy","text"]},"container_copy_copy_":{"columnClassNames":{"title_v2_copy_copy_c_1638553606":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_386496946":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1886340130":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_820364889":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499_402096092":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_1149904237":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1179862951":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_1648528305":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_295276270":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1120714341":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"analysis",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-5007684c06","type":"heading3","lines":["IA générative et analyse des opinions"],":type":"snowflake-site/components/title-v2"},"text_copy_copy_295276270":{"id":"text-ed7d6676e4","text":"\u003Cp\u003EL’analyse des opinions a fait un pas de géant avec l’évolution rapide de l’\u003Ca href=\"https://www.snowflake.com/fr/fundamentals/generative-ai-architecture-models-applications/\"\u003EIA générative\u003C/a\u003E alimentée par les grands modèles de langage (LLM). Découvrez comment l’IA générative redéfinit et booste l’analyse des opinions.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy":{"id":"title-v2-3731afcff9","type":"heading4","lines":["Compréhension améliorée du contexte"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy":{"id":"text-1d891ca0be","text":"\u003Cp\u003ELes modèles traditionnels s’appuient sur des techniques de Bag-of-Words ou sur de simples frameworks de \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/deep-learning/\"\u003Edeep learning\u003C/a\u003E, qui peuvent passer à côté d’indices d’opinion subtils. Grâce aux LLM, l’IA générative excelle dans la capture d’émotions nuancées, de sarcasme et d’un contexte plus profond, pour résoudre un défi persistant dans l’analyse des opinions.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-a7709d33c5","type":"heading4","lines":["Apprentissage Zero-shot et Few-shot"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy":{"id":"text-86f1299cfd","text":"\u003Cp\u003EL’IA générative réduit la dépendance vis-à-vis de la référence dynamique de grands jeux de données d’entraînement en utilisant l’apprentissage « Zero-shot » et « Few-shot ». Elle peut analyser les opinions dans de nouveaux domaines, tels que la finance ou la santé, sans entraînement approfondi, ce qui accélère la mise en œuvre et l’adaptabilité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1638553606":{"id":"title-v2-04ad9eb749","type":"heading4","lines":["Analyse multilingue et multi-domaines"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1648528305":{"id":"text-7b940e862d","text":"\u003Cp\u003EContrairement aux systèmes existants qui nécessitent des modèles séparés pour différents langages, l’IA générative peut traiter les opinions dans plusieurs langages de manière fluide. Grâce à sa capacité de généralisation entre les différents secteurs, il est moins nécessaire de personnaliser le produit en fonction du domaine, ce qui le rend très polyvalent.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1120714341":{"id":"title-v2-ae31ccf51b","type":"heading4","lines":["Reconnaissance des émotions et des intentions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_820364889":{"id":"text-5bf4e086c6","text":"\u003Cp\u003EAu-delà de la simple classification des opinions (positives, négatives, neutres), l’IA générative identifie des émotions complexes comme la frustration, l’excitation ou la satisfaction. Elle peut également détecter une intention dans un texte, par exemple si un message véhicule une plainte, un éloge ou même du sarcasme, augmentant ainsi son applicabilité dans le service client.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_386496946":{"id":"title-v2-b2cfc5f1fc","type":"heading4","lines":["Augmentation et amélioration des données"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_1149904237":{"id":"text-3c76e22fa7","text":"\u003Cp\u003EL’IA générative peut générer des données synthétiques étiquetées pour améliorer les modèles d’opinions traditionnels et ainsi améliorer la précision. Elle peut également nettoyer les jeux de données sur les opinions en corrigeant les exemples mal étiquetés afin d’améliorer la \u003Ca href=\"https://www.snowflake.com/fr/fundamentals/a-practical-guide-to-data-quality/\"\u003Equalité des données\u003C/a\u003E.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1886340130":{"id":"title-v2-80f1bb7f46","type":"heading4","lines":["Analyse interactive et conversationnelle des opinions"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499":{"id":"text-dc1dc44887","text":"\u003Cp\u003EAlors que les classificateurs traditionnels fournissent des résultats statiques, l’IA générative permet d’interagir en temps réel en expliquant les scores d’opinion et en proposant du contexte. Elle est précieuse dans les systèmes d’IA conversationnels, tels que les chatbots, ou les flux de travail de service client.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1179862951":{"id":"title-v2-c1314dfb2e","type":"heading4","lines":["Apprentissage personnalisable et adaptatif"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499_402096092":{"id":"text-a874bb575c","text":"\u003Cp\u003ELes entreprises peuvent ajuster les modèles d’IA générative en fonction de modèles d’opinion spécifiques à leur secteur. En intégrant les commentaires des utilisateurs, ces modèles peuvent évoluer et s’adapter au fil du temps, améliorant ainsi la précision et la pertinence au fil de l’utilisation.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_copy_295276270","title_v2_copy_copy","text_copy","title_v2_copy_copy_c","text_copy_copy","title_v2_copy_copy_c_1638553606","text_copy_copy_1648528305","title_v2_copy_copy_c_1120714341","text_copy_copy_820364889","title_v2_copy_copy_c_386496946","text_copy_copy_1149904237","title_v2_copy_copy_c_1886340130","text_copy_copy_11499","title_v2_copy_copy_c_1179862951","text_copy_copy_11499_402096092"]},"container_1790538746":{"columnClassNames":{"container_copy_43816":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"types",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"title_v2":{"id":"title-v2-b1062bb4c5","type":"heading3","lines":["Types d’analyse des opinions"],":type":"snowflake-site/components/title-v2"},"container_copy_43816":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_29527":"aem-GridColumn aem-GridColumn--default--12","text_copy":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_497870316":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_1007903673":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont_558784833":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"how-rag-works",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"text_copy_copy_29527":{"id":"text-5151d85496","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003ELe type d’analyse des opinions qu’une entreprise choisit dépend de ses objectifs. Souhaite-t-elle découvrir les préférences de ses clients ? Obtenir des avis détaillés sur les produits ? Mesurer l’impact d’un événement récent sur la réputation de la marque ? En analysant les données textuelles, les entreprises peuvent mieux répondre aux commentaires de leurs clients, améliorer leurs offres et prendre des décisions éclairées. Voici trois types populaires d’analyse des opinions :\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy":{"id":"text-5be41f13a5","additionalClasses":"list--blue-bullets","text":"\u003Cdiv\u003E\u003Cb\u003E \u003C/b\u003E\u003C/div\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cb\u003EL’analyse des opinions au niveau d’un document \u003C/b\u003Eanalyse les opinions globales exprimées dans un document ou un texte, ce qui permet de comprendre de manière générale si le texte est positif, négatif ou neutre. Ce type est utile pour analyser les avis des clients, les publications sur les réseaux sociaux ou tout texte exprimant une opinion.\u003C/li\u003E\n\u003Cli\u003EL’\u003Cb\u003Eanalyse des opinions basée sur les aspects\u003C/b\u003E va au-delà de l’analyse documentaire pour comprendre les opinions liées à des composants ou des fonctionnalités spécifiques d’un produit ou d’un service. Par exemple, un client peut louer la vitesse d’une application, mais critiquer son interface utilisateur. Ce niveau de granularité aide les entreprises à se concentrer sur ce qui favorise l’insatisfaction des clients, favorisant ainsi des améliorations plus intelligentes des produits.\u003C/li\u003E\n\u003Cli\u003E\u003Cb\u003EL’analyse des opinions à l’échelle d’une entité\u003C/b\u003E se concentre sur l’analyse des sentiments associés à des entités spécifiques, comme des marques, des produits ou des individus. Elle révèle comment un sujet cible est perçu en temps réel. En suivant l’évolution des opinions, les entreprises peuvent déterminer l’impact des campagnes, suivre l’évolution de la réputation de la marque ou mieux comprendre le positionnement concurrentiel sur le marché.\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cdiv\u003E\u003Cb\u003E \u003C/b\u003E\u003C/div\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"flexible_column_cont":{"id":"flexible-column-container-6af1618079","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-1ccf29b4e4",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{},":itemsOrder":[]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-3bac5c0a1c",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy_copy_c":{"id":"title-v2-da1a00bfe9","type":"heading4","lines":["Analyse des opinions 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l’entité"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_497870316":{"id":"flexible-column-container-9713a24d6c","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-21f8d334dd",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy_copy_c":{"id":"title-v2-bd82a7b0d2","type":"heading4","lines":["Portée de l’analyse"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-6ba8a6560f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-84c9208ee8","text":"\u003Cp\u003EÉvalue les opinions générales d’un document ou d’un texte dans son ensemble.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_3":{"layout":"SIMPLE","id":"container-ce510ee5e9",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-b79d2838f8","text":"\u003Cp\u003EÉvalue les opinions relatives à des aspects ou fonctionnalités spécifiques.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_4":{"layout":"SIMPLE","id":"container-db7ea33db9",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-be54f3c00c","text":"\u003Cp\u003EAnalyse les opinions liées à des entités spécifiques comme des marques ou des produits.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_558784833":{"id":"flexible-column-container-4fe301c698","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-508ac96ba2",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy_copy_c":{"id":"title-v2-d9790d1046","type":"heading4","lines":["Cas d’usage"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-04db8a6653",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-64b767ba2c","text":"\u003Cp\u003EIdéal pour résumer les avis des clients ou les opinions sur les réseaux sociaux à un niveau général.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_3":{"layout":"SIMPLE","id":"container-04478e9b81",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-d9f5461e82","text":"\u003Cp\u003EAide les entreprises à comprendre quelles caractéristiques du produit suscitent la satisfaction ou des réclamations.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_4":{"layout":"SIMPLE","id":"container-8e99324794",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-15044f1ecd","text":"\u003Cp\u003ESuivi de la réputation de la marque, de l’impact des campagnes et du sentiment concurrentiel.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false},"flexible_column_cont_1007903673":{"id":"flexible-column-container-bd085b9129","type":"4-column-even","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"extra-small","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-9788a55484",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"title_v2_copy_copy_c":{"id":"title-v2-6f26656dce","type":"heading4","lines":["Granularité"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["title_v2_copy_copy_c"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-54ba0d9c36",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-acac2bb6f1","text":"\u003Cp\u003EFournit une classification générale des opinions (positive, négative, neutre).\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_3":{"layout":"SIMPLE","id":"container-c2a9b3343b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-e23600b2ee","text":"\u003Cp\u003EFournit des informations détaillées sur les opinions par aspect (par ex.,, vitesse du produit par rapport à la facilité d’utilisation).\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},"flexible_column_content_container_4":{"layout":"SIMPLE","id":"container-9601ebfbab",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"text_copy_copy_82036":{"id":"text-4ef9af75dd","text":"\u003Cp\u003ESe concentre sur l’évolution des opinions liées aux entités individuelles.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["text_copy_copy_82036"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["text_copy_copy_29527","text_copy","flexible_column_cont","flexible_column_cont_497870316","flexible_column_cont_558784833","flexible_column_cont_1007903673"]}},":itemsOrder":["title_v2","container_copy_43816"]},"container_copy_copy__1158523250":{"columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"challenges",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-4021f26579","type":"heading3","lines":["Défis de l’analyse des opinions"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-dd8e46e654","text":"\u003Cp\u003EL’analyse des opinions offre un immense potentiel pour recueillir des informations sur les clients et le marché, mais plusieurs défis stratégiques doivent être relevés pour obtenir des résultats précis et fiables.\u003C/p\u003E\n\u003Cp\u003EL’un des principaux défis consiste à détecter les sarcasmes et l’ironie. Les systèmes automatisés interprètent souvent mal ces formes d’expression, ce qui conduit à des résultats d’analyse des opinions inexacts. Pour y remédier, des techniques avancées de NLP sont nécessaires pour détecter et comprendre les subtiles complexités de ton et d’intention qui sous-tendent le texte.\u003C/p\u003E\n\u003Cp\u003EUn autre défi consiste à gérer la négation et les dépendances contextuelles. Des mots comme 'pas' ou 'non' peuvent inverser l’opinion d'une phrase. Les algorithmes sophistiqués peuvent interpréter la structure des phrases en contexte. Sans cette capacité, les algorithmes d’analyse des opinions peuvent classer incorrectement les déclarations avec négation, ce qui entraîne des informations erronées.\u003C/p\u003E\n\u003Cp\u003ELes nuances linguistiques et culturelles compliquent encore l’analyse des opinions. Chaque langue présente des caractéristiques linguistiques, des expressions idiomatiques et des contextes culturels uniques qui ont un impact sur l’analyse des opinions. Par exemple, certains mots ou expressions peuvent avoir des connotations différentes ou des implications émotionnelles différentes selon les cultures. Les LLM s’avèrent être une excellente solution pour l’analyse des opinions. Ils excellent à détecter le sarcasme et l’ironie, car ils sont entraînés sur de grandes quantités de textes divers, ce qui leur permet de reconnaître les indices contextuels, le ton et les modèles de langage nuancés auxquels les modèles traditionnels sont confrontés. Pour y remédier, les modèles d’opinion ont besoin d’un entraînement complet sur les canons multilingues et les jeux de données culturellement informés.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138"]},"container_copy_copy_445119867":{"columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"related",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"flexible_column_cont":{"id":"flexible-column-container-c88dc56613","type":"2-column-even","alignColumns":"match-height","containerMaxWidth":"extra-large","topPadding":"extra-small","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-ac4d8fd712",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2":{"id":"card-v2-ec78e04198","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Analyse des opinions sur les produits avec Snowflake Cortex sur les Iceberg Tables"],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=Roko9aqtzl8"},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Regarder la vidéo"},"image":{"id":"image","alt":"Sample alt text","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ae04ca5-62df-4e11-9224-59b9cb7dcf47/snowflake%25403x.png?preferwebp=true&quality=85","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2"]},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-2aa405887f",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-small",":items":{"card_v2_copy":{"id":"card-v2-0711db9833","configurationStatus":{"configured":true,"message":""},"layoutStyle":"vertical",":type":"snowflake-site/components/card-v2","title":{"id":"title","type":"heading4","lines":["Obtenez des informations à partir de vos données non structurées avec Snowflake Cortex AI "],":type":"snowflake-site/components/title-v2"},"button":{"id":"button","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"https://www.youtube.com/watch?v=r6gVZ5m-K8c "},"linkTargetContentType":"GENERIC","linkType":"SNOWFLAKE_EXTERNAL",":type":"snowflake-site/components/button","text":"Regarder la vidéo"},"image":{"id":"image","alt":"Sample alt text","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8ae04ca5-62df-4e11-9224-59b9cb7dcf47/snowflake%25403x.png?preferwebp=true&quality=85","lazyEnabled":true,"height":"351","width":"624",":type":"snowflake-site/components/image"},"type":"content-card"}},":itemsOrder":["card_v2_copy"]},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["flexible_column_cont"]},"container_copy_copy__683185918":{"columnClassNames":{"text_copy_copy_11499":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1714779250":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_11499_446974872":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","layout":"RESPONSIVE_GRID","columnCount":12,"id":"benefits",":type":"snowflake-site/components/container","appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small",":items":{"title_v2":{"id":"title-v2-794cd81349","type":"heading3","lines":["Quels sont les avantages de l’analyse des opinions ?"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138":{"id":"text-5d4b87c2bf","text":"\u003Cp\u003EL’analyse des opinions permet d’obtenir des informations stratégiques sur les opinions et commentaires des clients, ce qui donne aux entreprises un avantage stratégique pour comprendre les perceptions du marché et y répondre. En analysant les opinions, les entreprises peuvent extraire des informations exploitables pour éclairer à la fois les actions immédiates et la stratégie à long terme.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-146773996a","type":"heading4","lines":["Améliorer la réputation de la marque et la fidélité des clients"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499":{"id":"text-8762ebf837","text":"\u003Cp\u003EL’un des principaux avantages de l’analyse des opinions est sa capacité à renforcer la réputation de la marque et la satisfaction des clients. En identifiant à la fois les approbations positives et les expériences négatives dans l’opinion des clients, les entreprises peuvent résoudre les problèmes et les préoccupations rapidement. Cette capacité permet non seulement d’améliorer les produits ou les services, mais aussi de renforcer les relations avec les clients, ce qui permet d’accroître la satisfaction et la fidélité.\u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1714779250":{"id":"title-v2-99e4daacfb","type":"heading4","lines":["Obtenir des informations approfondies sur le marché et les concurrents"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_copy_11499_446974872":{"id":"text-b373031aa0","text":"\u003Cp\u003EAu-delà de la compréhension des commentaires des clients, l’analyse des opinions aide les entreprises à identifier les tendances du marché et à effectuer une analyse de leurs concurrents. 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