Accesses Azure’s Text Analytics Service for natural language processing on text.
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
- For better performance, requests to the Azure Text Analytics service are made in parallel, and are returned as soon as they complete. Consequently, detection results will not be returned in the same order as their associated requests.
- All confidence scores are returned between 0 and 1.
- The list of supported languages for each analysis type is found at: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/language-support
- 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
Configuration
Input Ports
Input
This transformer accepts any feature.
Output Ports
Output
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.
| _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. |
<Initiator>
The incoming feature is output through this port.
<Rejected>
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
Authentication
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.
|
| 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. |
Request
| Text |
The text to perform detection on. |
| Detection Type |
The type of operation to perform. Choices are:
|
The remaining parameters available depend on the value of the Request > Detection Type parameter. Parameters for each Detection Type are detailed below.
Parameters - Entities Detection
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 |
Parameters - Key Phrases Detection
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 |
Parameters - Language Detection
Detect Language Options
Language detection does not require any additional parameters.
Parameters - Sentiment Detection
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.
How to Set Parameter Values
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.
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.
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.
Content Types
| These functions manipulate and format strings. | |
|
Special Characters |
A set of control characters is available in the Text Editor. |
| Math functions are available in both editors. | |
| Date/Time Functions | Date and time functions are available in the Text Editor. |
| These operators are available in the Arithmetic Editor. | |
| These return primarily feature-specific values. | |
| 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.
Table Tools
|
Row Reordering
|
Enabled once you have clicked on a row item. Choices include:
|
|
Cut, Copy, and Paste
|
Enabled once you have clicked on a row item. Choices include:
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 Holding |
No |
| Dependencies | Azure Cognitive Services Account |
| FME Licensing Level | FME Base Edition and above |
| Aliases | |
| History | Released FME 2019.2 |
FME Community
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Examples may contain information licensed under the Open Government Licence – Vancouver and/or the Open Government Licence – Canada.