Analyse de texte

Service d’IA de « text mining » (exploration de texte) qui détecte des insights tels que l’analyse des sentiments, les entités, les relations et les expressions clés dans du texte non structuré.

Explorez les insights d’un texte

Mine insights in unstructured text using natural language processing (NLP)—no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. Identify key phrases and entities such as people, places, and organizations to understand common topics and trends. Classify medical terminology using domain-specific, pretrained models. Evaluate text in a wide range of languages.

Vaste extraction d’entité

Identify important concepts in text, including key phrases and named entities such as people, events, and organizations.

Analyse puissante des sentiments

Examine what customers are saying about your brand and analyze sentiments around specific topics through opinion mining.

Question answering

Get answers to questions from content such as FAQ, product manuals, blogs, and policies.

Déploiement flexible

Run Text Analytics anywhere—in the cloud, on-premises, or at the edge in containers.

Langues: English (niveau de confiance : 100 %)
Expressions clés: The Sirloin steak, Contoso Steakhouse, midtown NYC, dinner party, great menu, chief cook, John Doe, online menu, marvelous food, spot, owner, name, kitchen, dining, place, contososteakhouse, email, order, complaint
Sentiment:
Document
MIXED
86%
Positif
0%
Neutre
14%
Négatif
Phrase 1
POSITIVE
99%
Positif
1%
Neutre
0%
Négatif
Phrase 2
POSITIVE
100%
Positif
0%
Neutre
0%
Négatif
Phrase 3
POSITIVE
100%
Positif
0%
Neutre
0%
Négatif
Phrase 4
POSITIVE
100%
Positif
0%
Neutre
0%
Négatif
Phrase 5
POSITIVE
100%
Positif
0%
Neutre
0%
Négatif
Phrase 6
NEUTRAL
0%
Positif
100%
Neutre
0%
Négatif
Phrase 7
NEGATIVE
0%
Positif
0%
Neutre
100%
Négatif
Phrase 8
POSITIVE
100%
Positif
0%
Neutre
0%
Négatif
Entités nommées: Contoso Steakhouse [Location]
midtown [Location-GPE]
NYC [Location-GPE]
last week [DateTime-DateRange]
dinner party [Event]
chief cook [PersonType]
owner [PersonType]
John Doe [Person]
kitchen [Location-Structural]
Sirloin steak [Product]
www.contososteakhouse.com [URL]
312-555-0176 [Phone Number]
order@contososteakhouse.com [Email]
food [Product]
Entités PII: Type: Organization
Value: Contoso

Type: DateTime
Value: last week

Type: PersonType
Value: chief cook

Type: PersonType
Value: owner

Type: Person
Value: John Doe

Type: Phone Number
Value: 312-555-0176

Type: Email
Value: order@contososteakhouse.com

Type: Organization
Value: contososteakhouse

Entités liées: We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore the spot! They provide marvelous food and they have a great menu. The chief cook happens to be the owner (I think his name is John Doe) and he is super nice, coming out of the kitchen and greeted us all. We enjoyed very much dining in the place! The Sirloin steak I ordered was tender and juicy, and the place was impeccably clean. You can even pre-order from their online menu at www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! The only complaint I have is the food didn't come fast enough. Overall I highly recommend it!
{
  "languageDetection": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "detectedLanguage": {
          "name": "English",
          "iso6391Name": "en",
          "confidenceScore": 0.99
        }
      }
    ],
    "errors": [],
    "modelversion": "2021-01-05"
  },
  "keyPhrases": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "keyPhrases": [
          "The Sirloin steak",
          "Contoso Steakhouse",
          "midtown NYC",
          "dinner party",
          "great menu",
          "chief cook",
          "John Doe",
          "online menu",
          "marvelous food",
          "spot",
          "owner",
          "name",
          "kitchen",
          "dining",
          "place",
          "contososteakhouse",
          "email",
          "order",
          "complaint"
        ]
      }
    ],
    "errors": [],
    "modelversion": "2021-06-01"
  },
  "sentiment": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "sentiment": "mixed",
        "confidenceScores": {
          "positive": 0.86,
          "neutral": 0.0,
          "negative": 0.14
        },
        "sentences": [
          {
            "sentiment": "positive",
            "confidenceScores": {
              "positive": 0.99,
              "neutral": 0.01,
              "negative": 0.0
            },
            "offset": 0,
            "length": 105
          },
          {
            "sentiment": "positive",
            "confidenceScores": {
              "positive": 1.0,
              "neutral": 0.0,
              "negative": 0.0
            },
            "offset": 106,
            "length": 55
          },
          {
            "sentiment": "positive",
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            },
            "offset": 162,
            "length": 137
          },
          {
            "sentiment": "positive",
            "confidenceScores": {
              "positive": 1.0,
              "neutral": 0.0,
              "negative": 0.0
            },
            "offset": 300,
            "length": 41
          },
          {
            "sentiment": "positive",
            "confidenceScores": {
              "positive": 1.0,
              "neutral": 0.0,
              "negative": 0.0
            },
            "offset": 342,
            "length": 85
          },
          {
            "sentiment": "neutral",
            "confidenceScores": {
              "positive": 0.0,
              "neutral": 1.0,
              "negative": 0.0
            },
            "offset": 428,
            "length": 139
          },
          {
            "sentiment": "negative",
            "confidenceScores": {
              "positive": 0.0,
              "neutral": 0.0,
              "negative": 1.0
            },
            "offset": 568,
            "length": 62
          },
          {
            "sentiment": "positive",
            "confidenceScores": {
              "positive": 1.0,
              "neutral": 0.0,
              "negative": 0.0
            },
            "offset": 631,
            "length": 30
          }
        ]
      }
    ],
    "errors": [],
    "modelversion": "2020-04-01"
  },
  "entities": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "entities": [
          {
            "text": "Contoso Steakhouse",
            "category": "Location",
            "subcategory": null,
            "offset": 11,
            "length": 18,
            "confidencescore": 0.78
          },
          {
            "text": "midtown",
            "category": "Location",
            "subcategory": "GPE",
            "offset": 41,
            "length": 7,
            "confidencescore": 0.63
          },
          {
            "text": "NYC",
            "category": "Location",
            "subcategory": "GPE",
            "offset": 49,
            "length": 3,
            "confidencescore": 0.81
          },
          {
            "text": "last week",
            "category": "DateTime",
            "subcategory": "DateRange",
            "offset": 53,
            "length": 9,
            "confidencescore": 0.8
          },
          {
            "text": "dinner party",
            "category": "Event",
            "subcategory": null,
            "offset": 69,
            "length": 12,
            "confidencescore": 0.93
          },
          {
            "text": "chief cook",
            "category": "PersonType",
            "subcategory": null,
            "offset": 166,
            "length": 10,
            "confidencescore": 0.88
          },
          {
            "text": "owner",
            "category": "PersonType",
            "subcategory": null,
            "offset": 195,
            "length": 5,
            "confidencescore": 0.98
          },
          {
            "text": "John Doe",
            "category": "Person",
            "subcategory": null,
            "offset": 222,
            "length": 8,
            "confidencescore": 1.0
          },
          {
            "text": "kitchen",
            "category": "Location",
            "subcategory": "Structural",
            "offset": 272,
            "length": 7,
            "confidencescore": 0.95
          },
          {
            "text": "Sirloin steak",
            "category": "Product",
            "subcategory": null,
            "offset": 346,
            "length": 13,
            "confidencescore": 0.9
          },
          {
            "text": "www.contososteakhouse.com",
            "category": "URL",
            "subcategory": null,
            "offset": 477,
            "length": 25,
            "confidencescore": 0.8
          },
          {
            "text": "312-555-0176",
            "category": "Phone Number",
            "subcategory": null,
            "offset": 509,
            "length": 12,
            "confidencescore": 0.8
          },
          {
            "text": "order@contososteakhouse.com",
            "category": "Email",
            "subcategory": null,
            "offset": 539,
            "length": 27,
            "confidencescore": 0.8
          },
          {
            "text": "food",
            "category": "Product",
            "subcategory": null,
            "offset": 601,
            "length": 4,
            "confidencescore": 0.68
          }
        ]
      }
    ],
    "errors": [],
    "modelversion": "2021-06-01"
  },
  "entityLinking": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "entities": [
          {
            "name": "Steakhouse",
            "matches": [
              {
                "text": "Steakhouse",
                "offset": 19,
                "length": 10,
                "score": 0.0
              }
            ],
            "language": "en",
            "id": "Steakhouse",
            "url": "https://en.wikipedia.org/wiki/Steakhouse",
            "datasource": "Wikipedia"
          },
          {
            "name": "New York City",
            "matches": [
              {
                "text": "NYC",
                "offset": 49,
                "length": 3,
                "score": 0.0
              }
            ],
            "language": "en",
            "id": "New York City",
            "url": "https://en.wikipedia.org/wiki/New_York_City",
            "datasource": "Wikipedia"
          },
          {
            "name": "John Doe",
            "matches": [
              {
                "text": "John Doe",
                "offset": 222,
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                "score": 0.0
              }
            ],
            "language": "en",
            "id": "John Doe",
            "url": "https://en.wikipedia.org/wiki/John_Doe",
            "datasource": "Wikipedia"
          },
          {
            "name": "Sirloin steak",
            "matches": [
              {
                "text": "Sirloin steak",
                "offset": 346,
                "length": 13,
                "score": 0.0
              }
            ],
            "language": "en",
            "id": "Sirloin steak",
            "url": "https://en.wikipedia.org/wiki/Sirloin_steak",
            "datasource": "Wikipedia"
          }
        ]
      }
    ],
    "errors": [],
    "modelversion": "2021-06-01"
  },
  "entityPII": {
    "documents": [
      {
        "id": "f924feec-f3e6-4ab8-8e26-b91a05cc3b82",
        "entities": [
          {
            "text": "Contoso",
            "category": "Organization",
            "subcategory": null,
            "offset": 11,
            "length": "7",
            "confidencescore": 0.58
          },
          {
            "text": "last week",
            "category": "DateTime",
            "subcategory": "DateRange",
            "offset": 53,
            "length": "9",
            "confidencescore": 0.8
          },
          {
            "text": "chief cook",
            "category": "PersonType",
            "subcategory": null,
            "offset": 166,
            "length": "10",
            "confidencescore": 0.63
          },
          {
            "text": "owner",
            "category": "PersonType",
            "subcategory": null,
            "offset": 195,
            "length": "5",
            "confidencescore": 0.93
          },
          {
            "text": "John Doe",
            "category": "Person",
            "subcategory": null,
            "offset": 222,
            "length": "8",
            "confidencescore": 0.98
          },
          {
            "text": "312-555-0176",
            "category": "Phone Number",
            "subcategory": null,
            "offset": 509,
            "length": "12",
            "confidencescore": 0.8
          },
          {
            "text": "order@contososteakhouse.com",
            "category": "Email",
            "subcategory": null,
            "offset": 539,
            "length": "27",
            "confidencescore": 0.8
          },
          {
            "text": "contososteakhouse",
            "category": "Organization",
            "subcategory": null,
            "offset": 545,
            "length": "17",
            "confidencescore": 0.45
          }
        ]
      }
    ],
    "errors": [],
    "modelversion": "2021-01-15"
  }
}

Identifier et classer les concepts importants

Extract a broad range of prebuilt entities such as people, places, organizations, dates/times, numerals, and over 100 types of personally identifiable information (PII), including protected health information (PHI), in documents using named entity recognition.

Extraire des phrases clés de texte non structuré

Quickly evaluate and identify the main points in unstructured text. Get a list of relevant phrases that best describe the subject of each record using key phrase extraction. Easily organize information to make sense of important topics and trends.

Mieux comprendre la perception du client

Analyze positive and negative sentiment in social media, customer reviews, and other sources to get a pulse on your brand. Use opinion mining to explore customers' perception of specific attributes of products or services in text.

Traitez des données médicales non structurées

Extract insights from unstructured clinical documents such as doctors' notes, electronic health records, and patient intake forms using the health feature of Text Analytics in preview. Recognize, classify, and determine relationships between medical concepts such as diagnosis, symptoms, and dosage and frequency of medication.

Create a conversational layer over your data

Get answers to questions from semi-structured and unstructured content such as URLs, FAQ, product manuals, blogs, support documents, and more.

Deploy anywhere, in the cloud or at the edge

Run Text Analytics wherever your data resides. Build applications that are optimized for both robust cloud capabilities and edge locality using containers.

Confidentialité et sécurité complètes

  • Your data stays yours. Microsoft doesn't use the training performed on your text to improve models.
  • Choisissez où Cognitive Services traite vos données avec des conteneurs.
  • Adossée à l’infrastructure Azure, l’API Analyse de texte offre une sécurité, une disponibilité, une conformité et une facilité de gestion de classe Entreprise.

Bénéficiez de la puissance, du contrôle et de la personnalisation dont vous avez besoin grâce à une tarification flexible

  • Pay as you go based on the number of transactions, with no upfront costs.

Ressources et documentation pour l’API Analyse de texte

Prise en main des ressources d’apprentissage

Explorez les ressources appréciées des développeurs

Approuvé par des organisations de toutes tailles

KPMG simplifie l’analyse des fraudes

KPMG aide les institutions financières à économiser des millions de dollars en lien avec la conformité grâce à sa solution Customer Risk Analytics, qui utilise l’API Analyse de texte afin de détecter des modèles et des mots clés pour épingler des risques de conformité.

KPMG

Wilson Allen extrait des informations des données non structurées

Wilson Allen a créé une solution d’IA puissante qui peut aider les entreprises de services légaux et professionnels du monde entier à trouver des niveaux d’informations sans précédent dans des données précédemment cloisonnées et non structurées.

Wilson Allen

IHC outille les ingénieurs du service

Royal IHC utilise le service Recherche cognitive Azure et l’API Analyse de texte pour décharger ses ingénieurs des tâches fastidieuses liées à la recherche manuelle de donnée dans des sources disparates, et leur fournir des informations sur leurs données tant structurées que non structurées.

Royal IHC

LaLiga renforce l’implication des aficionados

LaLiga is engaging millions of fans around the world with a personal digital assistant, using Text Analytics to process incoming queries and determine user intent in multiple languages.

LaLiga

TIBCO apporte une analyse de la cause racine

TIBCO utilise Analyse de texte et Détecteur d’anomalies pour détecter et analyser les anomalies (des changements soudains dans les modèles de données, identification des causes racines) et fournit des suggestions d’actions.

TIBCO

Kotak Mahindra Bank accélère la productivité

Kotak Asset Management transforme la gestion des services clients en activant des bots conversationnels facilitant l’analyse de la ligne d’objet, des informations client et du contenu du courrier afin d’identifier les sentiments et de déclencher la meilleure réaction possible.

Kotak

Questions fréquentes sur Analyse de texte

  • Text Analytics detects a wide range of languages, variants, and dialects. See the language support documentation for more information.
  • Yes. Sentiment analysis and key phrase extraction are available for a select number of languages, and you may request additional languages in the Text Analytics Forum.
  • L’extraction de phrases clés élimine les mots non essentiels et les adjectifs isolés. Des combinaisons adjectif-nom, telles que « vues spectaculaires » ou « temps brumeux », sont renvoyées ensemble. En règle générale, la sortie est constituée de substantifs et d’objets de la phrase, répertoriés par ordre d’importance. L’importance d’un concept est mesurée au nombre de ses occurrences, ou à sa relation avec d’autres éléments dans le texte.
  • Les améliorations apportées à des modèles et algorithmes sont annoncées quand elles revêtent une importance majeure, ou simplement ajoutées au service lorsque leur importance est mineure. Au fil du temps, vous constaterez peut-être qu’une même entrée de texte engendre un score de sentiment ou une sortie de phrase clé différents. Il s’agit d’une conséquence normale et intentionnelle de l’utilisation de ressources d’apprentissage automatique dans le cloud.
  • Yes, you can use the analyze operation in preview to combine more than one Text Analytics feature in the same asynchronous call. The analyze operation is currently only available in the Standard pricing tier and follows the same pricing criteria.

Prise en main de l’API Analyse de texte