[ aws . comprehend ]
detect-targeted-sentiment¶
Description¶
Inspects the input text and returns a sentiment analysis for each entity identified in the text.
For more information about targeted sentiment, see Targeted sentiment in the Amazon Comprehend Developer Guide .
See also: AWS API Documentation
Synopsis¶
detect-targeted-sentiment
--text <value>
--language-code <value>
[--cli-input-json | --cli-input-yaml]
[--generate-cli-skeleton <value>]
[--debug]
[--endpoint-url <value>]
[--no-verify-ssl]
[--no-paginate]
[--output <value>]
[--query <value>]
[--profile <value>]
[--region <value>]
[--version <value>]
[--color <value>]
[--no-sign-request]
[--ca-bundle <value>]
[--cli-read-timeout <value>]
[--cli-connect-timeout <value>]
[--cli-binary-format <value>]
[--no-cli-pager]
[--cli-auto-prompt]
[--no-cli-auto-prompt]
Options¶
--text (string)
A UTF-8 text string. The maximum string length is 5 KB.
--language-code (string)
The language of the input documents. Currently, English is the only supported language.
Possible values:
enesfrdeitptarhijakozhzh-TW
--cli-input-json | --cli-input-yaml (string)
Reads arguments from the JSON string provided. The JSON string follows the format provided by --generate-cli-skeleton. If other arguments are provided on the command line, those values will override the JSON-provided values. It is not possible to pass arbitrary binary values using a JSON-provided value as the string will be taken literally. This may not be specified along with --cli-input-yaml.
--generate-cli-skeleton (string)
Prints a JSON skeleton to standard output without sending an API request. If provided with no value or the value input, prints a sample input JSON that can be used as an argument for --cli-input-json. Similarly, if provided yaml-input it will print a sample input YAML that can be used with --cli-input-yaml. If provided with the value output, it validates the command inputs and returns a sample output JSON for that command. The generated JSON skeleton is not stable between versions of the AWS CLI and there are no backwards compatibility guarantees in the JSON skeleton generated.
Global Options¶
--debug (boolean)
Turn on debug logging.
--endpoint-url (string)
Override command’s default URL with the given URL.
--no-verify-ssl (boolean)
By default, the AWS CLI uses SSL when communicating with AWS services. For each SSL connection, the AWS CLI will verify SSL certificates. This option overrides the default behavior of verifying SSL certificates.
--no-paginate (boolean)
Disable automatic pagination. If automatic pagination is disabled, the AWS CLI will only make one call, for the first page of results.
--output (string)
The formatting style for command output.
- json
- text
- table
- yaml
- yaml-stream
--query (string)
A JMESPath query to use in filtering the response data.
--profile (string)
Use a specific profile from your credential file.
--region (string)
The region to use. Overrides config/env settings.
--version (string)
Display the version of this tool.
--color (string)
Turn on/off color output.
- on
- off
- auto
--no-sign-request (boolean)
Do not sign requests. Credentials will not be loaded if this argument is provided.
--ca-bundle (string)
The CA certificate bundle to use when verifying SSL certificates. Overrides config/env settings.
--cli-read-timeout (int)
The maximum socket read time in seconds. If the value is set to 0, the socket read will be blocking and not timeout. The default value is 60 seconds.
--cli-connect-timeout (int)
The maximum socket connect time in seconds. If the value is set to 0, the socket connect will be blocking and not timeout. The default value is 60 seconds.
--cli-binary-format (string)
The formatting style to be used for binary blobs. The default format is base64. The base64 format expects binary blobs to be provided as a base64 encoded string. The raw-in-base64-out format preserves compatibility with AWS CLI V1 behavior and binary values must be passed literally. When providing contents from a file that map to a binary blob fileb:// will always be treated as binary and use the file contents directly regardless of the cli-binary-format setting. When using file:// the file contents will need to properly formatted for the configured cli-binary-format.
- base64
- raw-in-base64-out
--no-cli-pager (boolean)
Disable cli pager for output.
--cli-auto-prompt (boolean)
Automatically prompt for CLI input parameters.
--no-cli-auto-prompt (boolean)
Disable automatically prompt for CLI input parameters.
Examples¶
Note
To use the following examples, you must have the AWS CLI installed and configured. See the Getting started guide in the AWS CLI User Guide for more information.
Unless otherwise stated, all examples have unix-like quotation rules. These examples will need to be adapted to your terminal’s quoting rules. See Using quotation marks with strings in the AWS CLI User Guide .
To detect the targeted sentiment of named entities in an input text
The following detect-targeted-sentiment example analyzes the input text and returns the named entities in addition to the targeted sentiment associated with each entity.
The pre-trained models confidence score for each prediction is also output.
aws comprehend detect-targeted-sentiment \
--language-code en \
--text "I do not enjoy January because it is too cold but August is the perfect temperature"
Output:
{
"Entities": [
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9999979734420776,
"GroupScore": 1.0,
"Text": "I",
"Type": "PERSON",
"MentionSentiment": {
"Sentiment": "NEUTRAL",
"SentimentScore": {
"Positive": 0.0,
"Negative": 0.0,
"Neutral": 1.0,
"Mixed": 0.0
}
},
"BeginOffset": 0,
"EndOffset": 1
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9638869762420654,
"GroupScore": 1.0,
"Text": "January",
"Type": "DATE",
"MentionSentiment": {
"Sentiment": "NEGATIVE",
"SentimentScore": {
"Positive": 0.0031610000878572464,
"Negative": 0.9967250227928162,
"Neutral": 0.00011100000119768083,
"Mixed": 1.9999999949504854e-06
}
},
"BeginOffset": 15,
"EndOffset": 22
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
{
"Score": 0.9664419889450073,
"GroupScore": 1.0,
"Text": "August",
"Type": "DATE",
"MentionSentiment": {
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 0.9999549984931946,
"Negative": 3.999999989900971e-06,
"Neutral": 4.099999932805076e-05,
"Mixed": 0.0
}
},
"BeginOffset": 50,
"EndOffset": 56
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9803199768066406,
"GroupScore": 1.0,
"Text": "temperature",
"Type": "ATTRIBUTE",
"MentionSentiment": {
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 1.0,
"Negative": 0.0,
"Neutral": 0.0,
"Mixed": 0.0
}
},
"BeginOffset": 77,
"EndOffset": 88
}
]
}
]
}
For more information, see Targeted Sentiment in the Amazon Comprehend Developer Guide.
Output¶
Entities -> (list)
Targeted sentiment analysis for each of the entities identified in the input text.
(structure)
Information about one of the entities found by targeted sentiment analysis.
For more information about targeted sentiment, see Targeted sentiment in the Amazon Comprehend Developer Guide .
DescriptiveMentionIndex -> (list)
One or more index into the Mentions array that provides the best name for the entity group.
(integer)
Mentions -> (list)
An array of mentions of the entity in the document. The array represents a co-reference group. See Co-reference group for an example.
(structure)
Information about one mention of an entity. The mention information includes the location of the mention in the text and the sentiment of the mention.
For more information about targeted sentiment, see Targeted sentiment in the Amazon Comprehend Developer Guide .
Score -> (float)
Model confidence that the entity is relevant. Value range is zero to one, where one is highest confidence.GroupScore -> (float)
The confidence that all the entities mentioned in the group relate to the same entity.Text -> (string)
The text in the document that identifies the entity.Type -> (string)
The type of the entity. Amazon Comprehend supports a variety of entity types .MentionSentiment -> (structure)
Contains the sentiment and sentiment score for the mention.
Sentiment -> (string)
The sentiment of the mention.SentimentScore -> (structure)
Describes the level of confidence that Amazon Comprehend has in the accuracy of its detection of sentiments.
Positive -> (float)
The level of confidence that Amazon Comprehend has in the accuracy of its detection of thePOSITIVEsentiment.Negative -> (float)
The level of confidence that Amazon Comprehend has in the accuracy of its detection of theNEGATIVEsentiment.Neutral -> (float)
The level of confidence that Amazon Comprehend has in the accuracy of its detection of theNEUTRALsentiment.Mixed -> (float)
The level of confidence that Amazon Comprehend has in the accuracy of its detection of theMIXEDsentiment.BeginOffset -> (integer)
The offset into the document text where the mention begins.EndOffset -> (integer)
The offset into the document text where the mention ends.