AzureTextAnalyticsConnector

Categories

Web

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RekognitionConnector

Accesses Azure’s Text Analytics Service for natural language processing on text.

Jump to Configuration

Typical Uses

Submitting text to the Azure Text Analytics service to

  • detect dominant language
  • get sentiment information
  • extract keywords

How does it work?

The AzureTextAnalyticsConnector uses your Azure Cognitive Services account credentials (either via a previously defined FME web connection, or by setting up a new FME web connection right from the transformer) to access the natural language processing service.

It will submit text to the service, and return features with attributes about that text. Each input feature may result in several output features.

Usage Notes

Configuration

Input Ports

This transformer accepts any feature.

Output Ports

Output will depend on the analysis chosen.

Language Detection

Detects the dominant language for text. The service may return multiple language guesses for an individual request.

Attributes

_language_code

The language code guessed for the text (for example, en).

_language_name

The name of the language (for example, English).

_confidence

The probability that a given prediction is correct.

_text

The text analyzed.

Sentiment Detection

Detects the sentiment for text.

Attributes

_sentiment

The sentiment for the text. Possible values are:

POSITIVE, NEGATIVE, NEUTRAL

_sentiment_value

The sentiment indication. A value closer to 0 is negative, value near 0.5 is neutral and a value closer to 1 is positive.

_text

The text analyzed.

Key Phrases Detection

Detects the key phrases for text.

Attributes

_key_phrases{}

The important words/phrases in the text. Outputs as a list attribute.

_text

The text analyzed.

Entities Detection

Detects the entities of the text.

Attributes

_name

The name of the entity.

_type

The type of entity (for example, Person).

_sub_type

The sub type.

_wikipedia_id

The Wikipedia title for the entity.

_wikipedia_language

The iso language code of the Wikipedia article.

_wikipedia_url

The url to the Wikipedia page

_text

The text analyzed.

The incoming feature is output through this port.

Features that cause the operation to fail are output through this port. An fme_rejection_code attribute, having the value ERROR_DURING_PROCESSING, will be added, along with a more descriptive fme_rejection_message attribute which contains more specific details as to the reason for the failure.

Features can be rejected when the data limits for the Azure Text Analytics service are exceeded. A possible workaround is to reduce the size or number of characters in the request. The Decelerator transformer can be used to prevent the service's rate limit from being exceeded.

The list of data limits for the Azure Text Analytics service is found at: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview#data-limits

Note: If a feature comes in to the AzureTextAnalyticsConnector already having a value for fme_rejection_code, this value will be removed.

Rejected Feature Handling: can be set to either terminate the translation or continue running when it encounters a rejected feature. This setting is available both as a default FME option and as a workspace parameter.

Parameters

To use the AzureTextAnalyticsConnector or the AzureComputerVisionConnector you will need a Cognitive Services Account, then generate an endpoint and key to authenticate through our connectors.

Credential Source

The AzureTextAnalyticsConnector can use credentials from different sources. Using a web connection integrates best with FME, but in some cases, you may wish to use one of the other sources.

  • Web Connection - use an Azure Cognitive Services web connection stored in the FME web connections database
  • Embedded - embed an endpoint and secret key as parameters in the transformer

Account

Available when the credential source is Web Connection. To create a Azure Cognitive Services connection, click the 'Account' drop-down box and select 'Add Web Connection...'.

The connection can then be managed via Tools -> FME Options... -> Web Connections.

Endpoint and Secret Key

Available when the credential source is Embedded. An endpoint and secret key can be specified directly in the transformer instead of in a web connection.

Text

The text to perform detection on.

Detection Type

The type of operation to perform. Choices are:

  • Entities Detection: Detects entities for the text.
  • Key Phrases Detection: Detects key phrases for the text.
  • Language Detection: Detects language(s) for the text.
  • Sentiment Detection: Detects sentiment for the text.

The remaining parameters available depend on the value of the Request > Detection Type parameter. Parameters for each Detection Type are detailed below.

Detect Entities Options

Language

The language of the text. All text must be in the same language per transformer. The language code must be used for the attribute value (for example, “en”).

For more information on supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/language-support

Detect Key Phrases Options

Language

The language of the text. All text must be in the same language per transformer. The language code must be used for the attribute value (for example, “en”).

For more information on supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/language-support

Detect Language Options

Language detection does not require any additional parameters.

Detect Sentiment Options

Language

The language of the text. All text must be in the same language per transformer. The language code must be used for the attribute value (for example, “en”).

For more information on supported languages: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/language-support

Editing Transformer Parameters

Using a set of menu options, transformer parameters can be assigned by referencing other elements in the workspace. More advanced functions, such as an advanced editor and an arithmetic editor, are also available in some transformers. To access a menu of these options, click beside the applicable parameter. For more information, see Transformer Parameter Menu Options.

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters. There are a number of tools and shortcuts that can assist in constructing values, generally available from the drop-down context menu adjacent to the value field.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

String Functions

These functions manipulate and format strings.

Special Characters

 A set of control characters is available in the Text Editor.

Math Functions

Math functions are available in both editors.
Date/Time Functions Date and time functions are available in the Text Editor.

Math Operators

These operators are available in the Arithmetic Editor.

FME Feature Functions

These return primarily feature-specific values.

FME Parameters

FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters Create your own editable parameters.

Dialog Options - Tables

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

  • Add a row
  • Remove a row
  • Move current row up one
  • Move current row down one
  • Move current row to top
  • Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

  • Cut a row - delete and copy to clipboard
  • Copy a row to the clipboard
  • Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

Reference

Processing Behavior

Feature-Based

Feature Holding

No
Dependencies Azure Cognitive Services Account
Aliases  
History Released FME 2019.2

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Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.