Text Analytics

Detect sentiment, key phrases, named entities and language from your text

Extract information from your text

Use the demo below to experiment with the Text Analytics API. Pick one of our examples or provide your own. Identify the language, sentiment, key phrases, and entities (Preview) of your text by clicking "Analyze".

See it in action

  1. Analyzed text
  2. JSON
Languages: English (confidence: 100 %)
Key phrases: week, Space Needle, wonderful trip, Seattle, times
Sentiment:
84 %
Named Entities: Seattle [Location]
last week [DateTime-DateRange]
Space Needle [Location]
Space Needle [Organization]
2 [Quantity-Number]
Linked Entities: I had a wonderful trip to Seattle last week and even visited the Space Needle 2 times!
{
  "languageDetection": {
    "documents": [
      {
        "id": "5bd507fc-4e61-4b42-bfd1-2556d4a5a492",
        "detectedLanguages": [
          {
            "name": "English",
            "iso6391Name": "en",
            "score": 1.0
          }
        ]
      }
    ],
    "errors": []
  },
  "keyPhrases": {
    "documents": [
      {
        "id": "5bd507fc-4e61-4b42-bfd1-2556d4a5a492",
        "keyPhrases": [
          "week",
          "Space Needle",
          "wonderful trip",
          "Seattle",
          "times"
        ]
      }
    ],
    "errors": []
  },
  "sentiment": {
    "documents": [
      {
        "id": "5bd507fc-4e61-4b42-bfd1-2556d4a5a492",
        "score": 0.83725243806838989
      }
    ],
    "errors": []
  },
  "entities": {
    "documents": [
      {
        "id": "5bd507fc-4e61-4b42-bfd1-2556d4a5a492",
        "entities": [
          {
            "name": "Seattle",
            "matches": [
              {
                "text": "Seattle",
                "offset": 26,
                "length": 7
              }
            ],
            "wikipediaLanguage": "en",
            "wikipediaId": "Seattle",
            "wikipediaUrl": "https://en.wikipedia.org/wiki/Seattle",
            "bingId": "5fbba6b8-85e1-4d41-9444-d9055436e473",
            "type": "Location",
            "subType": null
          },
          {
            "name": "last week",
            "matches": [
              {
                "text": "last week",
                "offset": 34,
                "length": 9
              }
            ],
            "wikipediaLanguage": null,
            "wikipediaId": null,
            "wikipediaUrl": null,
            "bingId": null,
            "type": "DateTime",
            "subType": "DateRange"
          },
          {
            "name": "Space Needle",
            "matches": [
              {
                "text": "Space Needle",
                "offset": 65,
                "length": 12
              }
            ],
            "wikipediaLanguage": "en",
            "wikipediaId": "Space Needle",
            "wikipediaUrl": "https://en.wikipedia.org/wiki/Space_Needle",
            "bingId": "f8dd5b08-206d-2554-6e4a-893f51f4de7e",
            "type": "Location",
            "subType": null
          },
          {
            "name": "Space Needle",
            "matches": [
              {
                "text": "Space Needle",
                "offset": 65,
                "length": 12
              }
            ],
            "wikipediaLanguage": null,
            "wikipediaId": null,
            "wikipediaUrl": null,
            "bingId": null,
            "type": "Organization",
            "subType": null
          },
          {
            "name": "2",
            "matches": [
              {
                "text": "2",
                "offset": 78,
                "length": 1
              }
            ],
            "wikipediaLanguage": null,
            "wikipediaId": null,
            "wikipediaUrl": null,
            "bingId": null,
            "type": "Quantity",
            "subType": "Number"
          }
        ]
      }
    ],
    "errors": []
  }
}

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Sentiment analysis

The API returns a numeric score between 0 and 1. Scores close to 1 indicate positive sentiment, and scores close to 0 indicate negative sentiment. Sentiment score is generated using classification techniques. The input features of the classifier include n-grams, features generated from part-of-speech tags, and word embeddings. It is supported in a variety of languages.

Learn more

Key phrase extraction

The API returns a list of strings denoting the key talking points in the input text.

We employ techniques from Microsoft Office's sophisticated Natural Language Processing toolkit. English, German, Spanish, and Japanese text are supported.

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Language detection

The API returns the detected language and a numeric score between 0 and 1. Scores close to 1 indicate 100% certainty that the identified language is true. A total of 120 languages are supported.

Learn more

Named Entity Recognition

Detect all named entities in the text, such as organizations, people, and locations, and more. Entity Linking disambiguates distinct entities by associating text to additional information on the web. For example, use it to determine whether a term such as “times” refers to “The New York Times” or “Times Square.”

Learn more
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