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Text Analytics

Detect sentiment, key phrases 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 "Analyse".

See it in action

  1. Analysed text
  2. JSON
Languages: English (confidence: 100%)
Key phrases: Seattle, wonderful trip, Space Needle
Sentiment:
98 %
Linked Entities (Preview): I had a wonderful trip to Seattle and enjoyed seeing the Space Needle!
{
  "languageDetection": {
    "documents": [
      {
        "id": "a827bf2a-8e70-45fa-8359-1f355e3ab347",
        "detectedLanguages": [
          {
            "name": "English",
            "iso6391Name": "en",
            "score": 1.0
          }
        ]
      }
    ],
    "errors": []
  },
  "keyPhrases": {
    "documents": [
      {
        "id": "a827bf2a-8e70-45fa-8359-1f355e3ab347",
        "keyPhrases": [
          "Seattle",
          "wonderful trip",
          "Space Needle"
        ]
      }
    ],
    "errors": []
  },
  "sentiment": {
    "documents": [
      {
        "id": "a827bf2a-8e70-45fa-8359-1f355e3ab347",
        "score": 0.97037649154663086
      }
    ],
    "errors": []
  },
  "entities": {
    "documents": [
      {
        "id": "a827bf2a-8e70-45fa-8359-1f355e3ab347",
        "entities": [
          {
            "name": "Space Needle",
            "matches": [
              {
                "text": "the Space Needle",
                "offset": 53,
                "length": 16
              }
            ],
            "wikipediaLanguage": "en",
            "wikipediaId": "Space Needle",
            "wikipediaUrl": "https://en.wikipedia.org/wiki/Space_Needle",
            "bingId": "f8dd5b08-206d-2554-6e4a-893f51f4de7e"
          },
          {
            "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"
          }
        ]
      }
    ],
    "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.

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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.

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Identify entities in your text Preview

Detect all named entities in the text, such as organisations, 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.”

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