title	Use prebuilt Text Analytics with REST API
description	How to use prebuilt text analytics in Fabric with REST API
ms.author	lagayhar
ms.reviewer	ruxu
ms.topic	how-to
ms.date	08/20/2025
ms.update-cycle	180-days
ms.search.form
ms.collection	ce-skilling-ai-copilot

Use Azure Language in Foundry Tools text analytics in Fabric with REST API and SynapseML (preview)

[!INCLUDE feature-preview]

Azure Language in Foundry Tools is a Microsoft Foundry tool that enables you to perform text mining and text analysis with Natural Language Processing (NLP) features.

In this article, you learn how to use Language services directly in Microsoft Fabric to analyze text. By the end of this article, you're able to:

Detect sentiment labels at the sentence or document level
Identify the language for a given text input
Extract key phrases from a text
Identify different entities in text and categorize them into predefined classes or types

Prerequisites

[!INCLUDE prerequisites]

Create a new notebook.
Attach your notebook to a lakehouse. On the left side of your notebook, select Add to add an existing lakehouse or create a new one.

Note

This article uses Microsoft Fabric's built-in prebuilt Foundry Tools, which handle authentication automatically. You don't need to obtain a separate Foundry Tools key - the authentication is managed through your Fabric workspace. For more information, see Prebuilt AI models in Fabric (preview).

The code samples in this article use libraries that are preinstalled in Microsoft Fabric notebooks:

SynapseML: Preinstalled in Fabric notebooks for machine learning capabilities
PySpark: Available by default in Fabric Spark compute
Standard Python libraries: json, uuid are part of Python standard library

Note

Microsoft Fabric notebooks come with many common libraries preinstalled. The SynapseML library, which provides the MLflow integration and text analytics capabilities, is automatically available in the Spark environment.

Choose your approach

This article provides two ways to use Language services in Fabric:

REST API approach: Direct HTTP calls to the service (recommended for beginners)
SynapseML approach: Using Spark DataFrames for larger-scale processing

Tip

New users should start with the REST API approach as it's easier to understand and debug. The SynapseML approach is better for processing large datasets with Spark.

Rest API

Set up authentication and endpoints

Copy and paste this code into the first cell of your Fabric notebook to set up the connection to Language services:

Note

This code uses Fabric's built-in authentication. The get_fabric_env_config function automatically retrieves your workspace credentials and connects to the prebuilt Foundry Tools. No API key is required.

# Get workload endpoints and access token
from synapse.ml.fabric.service_discovery import get_fabric_env_config
from synapse.ml.fabric.token_utils import TokenUtils
import json
import requests

fabric_env_config = get_fabric_env_config().fabric_env_config
auth_header = TokenUtils().get_openai_auth_header()

# Make a RESful request to Foundry tool
prebuilt_AI_base_host = fabric_env_config.ml_workload_endpoint + "cognitive/textanalytics/"
print("Workload endpoint for Foundry tool: \n" + prebuilt_AI_base_host)

service_url = prebuilt_AI_base_host + "language/:analyze-text?api-version=2022-05-01"
print("Service URL: \n" + service_url)

auth_headers = {
    "Authorization" : auth_header
}

def print_response(response):
    if response.status_code == 200:
        print(json.dumps(response.json(), indent=2))
    else:
        print(f"Error: {response.status_code}, {response.content}")

SynapseML

Import required libraries

Copy and paste this code into the first cell of your Fabric notebook:

import synapse.ml.core
from synapse.ml.cognitive.language import AnalyzeText
from pyspark.sql.functions import col

# Note: 'spark' and 'display()' are automatically available in Fabric notebooks

Sentiment analysis

Rest API

The Sentiment Analysis feature provides a way for detecting the sentiment labels (such as "negative," "neutral" and "positive") and confidence scores at the sentence and document-level. This feature also returns confidence scores between 0 and 1 for each document and sentences within it for positive, neutral, and negative sentiment. See the Sentiment Analysis and Opinion Mining language support for the list of enabled languages.

Analyze sentiment of text

Copy this code into a new cell in your notebook to analyze the sentiment of a sample text:

payload = {
    "kind": "SentimentAnalysis",
    "parameters": {
        "modelVersion": "latest",
        "opinionMining": "True"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "language":"en",
                "text": "The food and service were unacceptable. The concierge was nice, however."
            }
        ]
    }
} 

response = requests.post(service_url, json=payload, headers=auth_headers)


# Output all information of the request process
print_response(response)

Tip

You can replace the text in the "text" field with your own content to analyze. The service returns sentiment scores and identifies which parts of the text are positive, negative, or neutral.

Expected output

When you run the following code successfully, you should see output similar to this:

{
  "kind": "SentimentAnalysisResults",
  "results": {
    "documents": [
      {
        "id": "1",
        "sentiment": "negative",
        "confidenceScores": {
          "positive": 0.0,
          "neutral": 0.0,
          "negative": 1.0
        },
        "sentences": [
          {
            "sentiment": "negative",
            "confidenceScores": {
              "positive": 0.0,
              "neutral": 0.0,
              "negative": 1.0
            },
            "offset": 0,
            "length": 40,
            "text": "The food and service were unacceptable. ",
            "targets": [
              {
                "sentiment": "negative",
                "confidenceScores": {
                  "positive": 0.01,
                  "negative": 0.99
                },
                "offset": 4,
                "length": 4,
                "text": "food",
                "relations": [
                  {
                    "relationType": "assessment",
                    "ref": "#/documents/0/sentences/0/assessments/0"
                  }
                ]
              },
              {
                "sentiment": "negative",
                "confidenceScores": {
                  "positive": 0.01,
                  "negative": 0.99
                },
                "offset": 13,
                "length": 7,
                "text": "service",
                "relations": [
                  {
                    "relationType": "assessment",
                    "ref": "#/documents/0/sentences/0/assessments/0"
                  }
                ]
              }
            ],
            "assessments": [
              {
                "sentiment": "negative",
                "confidenceScores": {
                  "positive": 0.01,
                  "negative": 0.99
                },
                "offset": 26,
                "length": 12,
                "text": "unacceptable",
                "isNegated": false
              }
            ]
          },
          {
            "sentiment": "neutral",
            "confidenceScores": {
              "positive": 0.22,
              "neutral": 0.75,
              "negative": 0.04
            },
            "offset": 40,
            "length": 32,
            "text": "The concierge was nice, however.",
            "targets": [
              {
                "sentiment": "positive",
                "confidenceScores": {
                  "positive": 1.0,
                  "negative": 0.0
                },
                "offset": 44,
                "length": 9,
                "text": "concierge",
                "relations": [
                  {
                    "relationType": "assessment",
                    "ref": "#/documents/0/sentences/1/assessments/0"
                  }
                ]
              }
            ],
            "assessments": [
              {
                "sentiment": "positive",
                "confidenceScores": {
                  "positive": 1.0,
                  "negative": 0.0
                },
                "offset": 58,
                "length": 4,
                "text": "nice",
                "isNegated": false
              }
            ]
          }
        ],
        "warnings": []
      }
    ],
    "errors": [],
    "modelVersion": "2025-01-01"
  }
}

SynapseML

The Sentiment Analysis feature provides a way for detecting the sentiment labels (such as "negative," "neutral" and "positive") and confidence scores at the sentence and document-level. This feature also returns confidence scores between 0 and 1 for each document & sentences within it for positive, neutral, and negative sentiment. See the Sentiment Analysis and Opinion Mining language support for the list of enabled languages.

df = spark.createDataFrame([
    ("Great atmosphere. Close to plenty of restaurants, hotels, and transit! Staff are friendly and helpful.",),
    ("What a sad story!",)
], ["text"])

model = (AnalyzeText()
        .setTextCol("text")
        .setKind("SentimentAnalysis")
        .setOutputCol("response"))

result = model.transform(df)\
        .withColumn("documents", col("response.documents"))\
        .withColumn("sentiment", col("documents.sentiment"))

display(result.select("text", "sentiment"))

Language detector

Rest API

The Language Detector evaluates text input for each document and returns language identifiers with a score that indicates the strength of the analysis. This capability is useful for content stores that collect arbitrary text, where language is unknown. See the Supported languages for language detection for the list of enabled languages.

payload = {
    "kind": "LanguageDetection",
    "parameters": {
        "modelVersion": "latest"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "text": "This is a document written in English."
            }
        ]
    }
}

response = requests.post(service_url, json=payload, headers=auth_headers)

# Output all information of the request process
print_response(response)

Output

{
  "kind": "LanguageDetectionResults",
  "results": {
    "documents": [
      {
        "id": "1",
        "warnings": [],
        "detectedLanguage": {
          "name": "English",
          "iso6391Name": "en",
          "confidenceScore": 0.95
        }
      }
    ],
    "errors": [],
    "modelVersion": "2024-11-01"
  }
}

SynapseML

df = spark.createDataFrame([
    (["Hello world"],),
    (["Bonjour tout le monde", "Hola mundo", "Tumhara naam kya hai?"],),
    (["你好"],),
    (["日本国（にほんこく、にっぽんこく、英"],)
], ["text"])

model = (AnalyzeText()
        .setTextCol("text")
        .setKind("LanguageDetection")
        .setOutputCol("response"))

result = model.transform(df)\
        .withColumn("documents", col("response.documents"))\
        .withColumn("detectedLanguage", col("documents.detectedLanguage.name"))

display(result.select("text", "detectedLanguage"))

Key Phrase Extractor

Rest API

The Key Phrase Extraction evaluates unstructured text and returns a list of key phrases. This capability is useful if you need to quickly identify the main points in a collection of documents. See the Supported languages for key phrase extraction for the list of enabled languages.

payload = {
    "kind": "KeyPhraseExtraction",
    "parameters": {
        "modelVersion": "latest"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "language":"en",
                "text": "Dr. Smith has a very modern medical office, and she has great staff."
            }
        ]
    }
}

response = requests.post(service_url, json=payload, headers=auth_headers)

# Output all information of the request process
print_response(response)

Output

{
  "kind": "KeyPhraseExtractionResults",
  "results": {
    "documents": [
      {
        "id": "1",
        "keyPhrases": [
          "modern medical office",
          "Dr. Smith",
          "great staff"
        ],
        "warnings": []
      }
    ],
    "errors": [],
    "modelVersion": "2022-10-01"
  }
}

SynapseML

df = spark.createDataFrame([
    ("en", "Microsoft was founded by Bill Gates and Paul Allen."),
    ("en", "Text Analytics is one of the Azure Cognitive Services."),
    ("en", "My cat might need to see a veterinarian.")
], ["language", "text"])

model = (AnalyzeText()
        .setTextCol("text")
        .setKind("KeyPhraseExtraction")
        .setOutputCol("response"))

result = model.transform(df)\
        .withColumn("documents", col("response.documents"))\
        .withColumn("keyPhrases", col("documents.keyPhrases"))

display(result.select("text", "keyPhrases"))

Named Entity Recognition (NER)

Rest API

Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into predefined classes or types such as: person, location, event, product, and organization. See the NER language support for the list of enabled languages.

payload = {
    "kind": "EntityRecognition",
    "parameters": {
        "modelVersion": "latest"
    },
    "analysisInput":{
        "documents":[
            {
                "id":"1",
                "language": "en",
                "text": "I had a wonderful trip to Seattle last week."
            }
        ]
    }
}

response = requests.post(service_url, json=payload, headers=auth_headers)

# Output all information of the request process
print_response(response)

Output

{
  "kind": "EntityRecognitionResults",
  "results": {
    "documents": [
      {
        "id": "1",
        "entities": [
          {
            "text": "trip",
            "category": "Event",
            "offset": 18,
            "length": 4,
            "confidenceScore": 0.66
          },
          {
            "text": "Seattle",
            "category": "Location",
            "subcategory": "City",
            "offset": 26,
            "length": 7,
            "confidenceScore": 1.0
          },
          {
            "text": "last week",
            "category": "DateTime",
            "subcategory": "DateRange",
            "offset": 34,
            "length": 9,
            "confidenceScore": 1.0
          }
        ],
        "warnings": []
      }
    ],
    "errors": [],
    "modelVersion": "2025-02-01"
  }
}

SynapseML

df = spark.createDataFrame([
    ("en", "Microsoft was founded by Bill Gates and Paul Allen."),
    ("en", "Pike place market is my favorite Seattle attraction.")
], ["language", "text"])

model = (AnalyzeText()
        .setTextCol("text")
        .setKind("EntityRecognition")
        .setOutputCol("response"))

result = model.transform(df)\
        .withColumn("documents", col("response.documents"))\
        .withColumn("entityNames", col("documents.entities.text"))

display(result.select("text", "entityNames"))

Entity linking

Rest API

No steps for REST API in this section.

SynapseML

Entity linking identifies and disambiguates the identity of entities found in text. For example, in the sentence "We went to Seattle last week.", the word "Seattle" would be identified, with a link to more information on Wikipedia. See Supported languages for entity linking for the list of enabled languages.

df = spark.createDataFrame([
    ("en", "Microsoft was founded by Bill Gates and Paul Allen."),
    ("en", "Pike place market is my favorite Seattle attraction.")
], ["language", "text"])

model = (AnalyzeText()
        .setTextCol("text")
        .setKind("EntityLinking")
        .setOutputCol("response"))

result = model.transform(df)\
        .withColumn("documents", col("response.documents"))\
        .withColumn("entityNames", col("documents.entities.name"))

display(result)

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Use Azure Language in Foundry Tools text analytics in Fabric with REST API and SynapseML (preview)

Prerequisites

Choose your approach

Rest API

Set up authentication and endpoints

SynapseML

Import required libraries

Sentiment analysis

Rest API

Analyze sentiment of text

Expected output

SynapseML

Language detector

Rest API

Output

SynapseML

Key Phrase Extractor

Rest API

Output

SynapseML

Named Entity Recognition (NER)

Rest API

Output

SynapseML

Entity linking

Rest API

SynapseML

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Use Azure Language in Foundry Tools text analytics in Fabric with REST API and SynapseML (preview)

Prerequisites

Choose your approach

Set up authentication and endpoints

Import required libraries

Sentiment analysis

Analyze sentiment of text

Expected output

Language detector

Output

Key Phrase Extractor

Output

Named Entity Recognition (NER)

Output

Entity linking

Related content