Text Analytics API (v3.1-preview.1)
The Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction and language detection. No training data is needed to use this API; just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions. Further documentation can be found in https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
Sentiment
The API returns a detailed sentiment analysis for the input text. The analysis is done in multiple levels of granularity, start from the a document level, down to sentence and key terms (aspects) and opinions.
Select the testing console in the region where you created your resource:
Open API testing consoleRequest URL
Request parameters
(optional)
string
(Optional) This value indicates which model will be used for scoring. If a model-version is not specified, the API should default to the latest, non-preview version.
(optional)
boolean
(Optional) if set to true, response will contain input and document level statistics.
(optional)
boolean
(Optional) if set to true, response will contain input and document level statistics including aspect-based sentiment analysis results.
(optional)
string
(Optional) Specifies the method used to interpret string offsets. Defaults to Text Elements (Graphemes) according to Unicode v8.0.0. For additional information see https://aka.ms/text-analytics-offsets.
Request headers
string
Media type of the body sent to the API.
string
Subscription key which provides access to this API. Found in your Cognitive Services accounts.
Request body
Collection of documents to analyze.
{
"documents": [
{
"language": "en",
"id": "1",
"text": "Great atmosphere. Close to plenty of restaurants, hotels, and transit! Staff are friendly and helpful."
},
{
"language": "en",
"id": "2",
"text": "Bad atmosphere. Not close to plenty of restaurants, hotels, and transit! Staff are not friendly and helpful."
}
]
}{
"type": "object",
"required": [
"documents"
],
"properties": {
"documents": {
"type": "array",
"description": "The set of documents to process as part of this batch.",
"items": {
"type": "object",
"required": [
"id",
"text"
],
"properties": {
"id": {
"type": "string",
"description": "A unique, non-empty document identifier."
},
"text": {
"type": "string",
"description": "The input text to process."
},
"language": {
"type": "string",
"description": "(Optional) This is the 2 letter ISO 639-1 representation of a language. For example, use \"en\" for English; \"es\" for Spanish etc. If not set, use \"en\" for English as default."
}
},
"description": "Contains an input document to be analyzed by the service."
}
}
},
"description": "Contains a set of input documents to be analyzed by the service."
}{
"documents": [
{
"language": "en",
"id": "1",
"text": "Great atmosphere. Close to plenty of restaurants, hotels, and transit! Staff are friendly and helpful."
},
{
"language": "en",
"id": "2",
"text": "Bad atmosphere. Not close to plenty of restaurants, hotels, and transit! Staff are not friendly and helpful."
}
]
}{
"type": "object",
"required": [
"documents"
],
"properties": {
"documents": {
"type": "array",
"description": "The set of documents to process as part of this batch.",
"items": {
"type": "object",
"required": [
"id",
"text"
],
"properties": {
"id": {
"type": "string",
"description": "A unique, non-empty document identifier."
},
"text": {
"type": "string",
"description": "The input text to process."
},
"language": {
"type": "string",
"description": "(Optional) This is the 2 letter ISO 639-1 representation of a language. For example, use \"en\" for English; \"es\" for Spanish etc. If not set, use \"en\" for English as default."
}
},
"description": "Contains an input document to be analyzed by the service."
}
}
},
"description": "Contains a set of input documents to be analyzed by the service."
}Response 200
A successful call results in a document sentiment prediction, as well as sentiment scores for each sentiment class (Positive, Negative, and Neutral)
{
"documents": [
{
"id": "1",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"sentences": [
{
"text": "Great atmosphere.",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"offset": 0,
"length": 17,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 6,
"length": 10,
"text": "atmosphere",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/0/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 0,
"length": 5,
"text": "great",
"isNegated": false
}
]
},
{
"text": "Close to plenty of restaurants, hotels, and transit!",
"sentiment": "neutral",
"confidenceScores": {
"positive": 0.13,
"neutral": 0.86,
"negative": 0.01
},
"offset": 18,
"length": 52,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 37,
"length": 11,
"text": "restaurants",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/1/opinions/0"
}
]
},
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 50,
"length": 6,
"text": "hotels",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/1/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 18,
"length": 15,
"text": "close to plenty",
"isNegated": false
}
]
},
{
"text": "Staff are friendly and helpful.",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"offset": 71,
"length": 31,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 71,
"length": 5,
"text": "Staff",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/2/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 81,
"length": 8,
"text": "friendly",
"isNegated": false
}
]
}
],
"warnings": []
},
{
"id": "2",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"neutral": 0.14,
"negative": 0.85
},
"sentences": [
{
"text": "Bad atmosphere.",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.0,
"negative": 1.0
},
"offset": 8,
"length": 15,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 4,
"length": 10,
"text": "atmosphere",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/0/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 0,
"length": 3,
"text": "bad",
"isNegated": false
}
]
},
{
"text": "Not close to plenty of restaurants, hotels, and transit!",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.02,
"neutral": 0.44,
"negative": 0.54
},
"offset": 16,
"length": 56,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.03,
"negative": 0.97
},
"offset": 39,
"length": 11,
"text": "restaurants",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/1/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.03,
"negative": 0.97
},
"offset": 20,
"length": 5,
"text": "close",
"isNegated": true
}
]
},
{
"text": "Staff are not friendly and helpful.",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.0,
"negative": 1.0
},
"offset": 73,
"length": 35,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"negative": 1.0
},
"offset": 73,
"length": 5,
"text": "Staff",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/2/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"negative": 1.0
},
"offset": 87,
"length": 8,
"text": "friendly",
"isNegated": true
}
]
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2020-04-01"
}{
"documents": [
{
"id": "1",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"sentences": [
{
"text": "Great atmosphere.",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"offset": 0,
"length": 17,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 6,
"length": 10,
"text": "atmosphere",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/0/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 0,
"length": 5,
"text": "great",
"isNegated": false
}
]
},
{
"text": "Close to plenty of restaurants, hotels, and transit!",
"sentiment": "neutral",
"confidenceScores": {
"positive": 0.13,
"neutral": 0.86,
"negative": 0.01
},
"offset": 18,
"length": 52,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 37,
"length": 11,
"text": "restaurants",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/1/opinions/0"
}
]
},
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 50,
"length": 6,
"text": "hotels",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/1/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 0.99,
"negative": 0.01
},
"offset": 18,
"length": 15,
"text": "close to plenty",
"isNegated": false
}
]
},
{
"text": "Staff are friendly and helpful.",
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"neutral": 0.0,
"negative": 0.0
},
"offset": 71,
"length": 31,
"aspects": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 71,
"length": 5,
"text": "Staff",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/0/sentences/2/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "positive",
"confidenceScores": {
"positive": 1.0,
"negative": 0.0
},
"offset": 81,
"length": 8,
"text": "friendly",
"isNegated": false
}
]
}
],
"warnings": []
},
{
"id": "2",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"neutral": 0.14,
"negative": 0.85
},
"sentences": [
{
"text": "Bad atmosphere.",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.0,
"negative": 1.0
},
"offset": 8,
"length": 15,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 4,
"length": 10,
"text": "atmosphere",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/0/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.01,
"negative": 0.99
},
"offset": 0,
"length": 3,
"text": "bad",
"isNegated": false
}
]
},
{
"text": "Not close to plenty of restaurants, hotels, and transit!",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.02,
"neutral": 0.44,
"negative": 0.54
},
"offset": 16,
"length": 56,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.03,
"negative": 0.97
},
"offset": 39,
"length": 11,
"text": "restaurants",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/1/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.03,
"negative": 0.97
},
"offset": 20,
"length": 5,
"text": "close",
"isNegated": true
}
]
},
{
"text": "Staff are not friendly and helpful.",
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"neutral": 0.0,
"negative": 1.0
},
"offset": 73,
"length": 35,
"aspects": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"negative": 1.0
},
"offset": 73,
"length": 5,
"text": "Staff",
"relations": [
{
"relationType": "opinion",
"ref": "#/documents/1/sentences/2/opinions/0"
}
]
}
],
"opinions": [
{
"sentiment": "negative",
"confidenceScores": {
"positive": 0.0,
"negative": 1.0
},
"offset": 87,
"length": 8,
"text": "friendly",
"isNegated": true
}
]
}
],
"warnings": []
}
],
"errors": [],
"modelVersion": "2020-04-01"
}Response 500
Error Response
{
"error": {
"code": "InternalServerError",
"message": "Processing failed unexpectedly. Please try again later."
}
}{
"error": {
"code": "InternalServerError",
"message": "Processing failed unexpectedly. Please try again later."
}
}Code samples
@ECHO OFF
curl -v -X POST "https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment?model-version={string}&showStats={boolean}&opinionMining={boolean}&stringIndexType={string}"
-H "Content-Type: application/json"
-H "Ocp-Apim-Subscription-Key: {subscription key}"
--data-ascii "{body}"
using System;
using System.Net.Http.Headers;
using System.Text;
using System.Net.Http;
using System.Web;
namespace CSHttpClientSample
{
static class Program
{
static void Main()
{
MakeRequest();
Console.WriteLine("Hit ENTER to exit...");
Console.ReadLine();
}
static async void MakeRequest()
{
var client = new HttpClient();
var queryString = HttpUtility.ParseQueryString(string.Empty);
// Request headers
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");
// Request parameters
queryString["model-version"] = "{string}";
queryString["showStats"] = "{boolean}";
queryString["opinionMining"] = "{boolean}";
queryString["stringIndexType"] = "{string}";
var uri = "https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment?" + queryString;
HttpResponseMessage response;
// Request body
byte[] byteData = Encoding.UTF8.GetBytes("{body}");
using (var content = new ByteArrayContent(byteData))
{
content.Headers.ContentType = new MediaTypeHeaderValue("< your content type, i.e. application/json >");
response = await client.PostAsync(uri, content);
}
}
}
} // // This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
import java.net.URI;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.utils.URIBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.util.EntityUtils;
public class JavaSample
{
public static void main(String[] args)
{
HttpClient httpclient = HttpClients.createDefault();
try
{
URIBuilder builder = new URIBuilder("https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment");
builder.setParameter("model-version", "{string}");
builder.setParameter("showStats", "{boolean}");
builder.setParameter("opinionMining", "{boolean}");
builder.setParameter("stringIndexType", "{string}");
URI uri = builder.build();
HttpPost request = new HttpPost(uri);
request.setHeader("Content-Type", "application/json");
request.setHeader("Ocp-Apim-Subscription-Key", "{subscription key}");
// Request body
StringEntity reqEntity = new StringEntity("{body}");
request.setEntity(reqEntity);
HttpResponse response = httpclient.execute(request);
HttpEntity entity = response.getEntity();
if (entity != null)
{
System.out.println(EntityUtils.toString(entity));
}
}
catch (Exception e)
{
System.out.println(e.getMessage());
}
}
}
<!DOCTYPE html>
<html>
<head>
<title>JSSample</title>
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
</head>
<body>
<script type="text/javascript">
$(function() {
var params = {
// Request parameters
"model-version": "{string}",
"showStats": "{boolean}",
"opinionMining": "{boolean}",
"stringIndexType": "{string}",
};
$.ajax({
url: "https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment?" + $.param(params),
beforeSend: function(xhrObj){
// Request headers
xhrObj.setRequestHeader("Content-Type","application/json");
xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key","{subscription key}");
},
type: "POST",
// Request body
data: "{body}",
})
.done(function(data) {
alert("success");
})
.fail(function() {
alert("error");
});
});
</script>
</body>
</html>
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[])
{
NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
NSString* path = @"https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment";
NSArray* array = @[
// Request parameters
@"entities=true",
@"model-version={string}",
@"showStats={boolean}",
@"opinionMining={boolean}",
@"stringIndexType={string}",
];
NSString* string = [array componentsJoinedByString:@"&"];
path = [path stringByAppendingFormat:@"?%@", string];
NSLog(@"%@", path);
NSMutableURLRequest* _request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:path]];
[_request setHTTPMethod:@"POST"];
// Request headers
[_request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
[_request setValue:@"{subscription key}" forHTTPHeaderField:@"Ocp-Apim-Subscription-Key"];
// Request body
[_request setHTTPBody:[@"{body}" dataUsingEncoding:NSUTF8StringEncoding]];
NSURLResponse *response = nil;
NSError *error = nil;
NSData* _connectionData = [NSURLConnection sendSynchronousRequest:_request returningResponse:&response error:&error];
if (nil != error)
{
NSLog(@"Error: %@", error);
}
else
{
NSError* error = nil;
NSMutableDictionary* json = nil;
NSString* dataString = [[NSString alloc] initWithData:_connectionData encoding:NSUTF8StringEncoding];
NSLog(@"%@", dataString);
if (nil != _connectionData)
{
json = [NSJSONSerialization JSONObjectWithData:_connectionData options:NSJSONReadingMutableContainers error:&error];
}
if (error || !json)
{
NSLog(@"Could not parse loaded json with error:%@", error);
}
NSLog(@"%@", json);
_connectionData = nil;
}
[pool drain];
return 0;
}
<?php
// This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
require_once 'HTTP/Request2.php';
$request = new Http_Request2('https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment');
$url = $request->getUrl();
$headers = array(
// Request headers
'Content-Type' => 'application/json',
'Ocp-Apim-Subscription-Key' => '{subscription key}',
);
$request->setHeader($headers);
$parameters = array(
// Request parameters
'model-version' => '{string}',
'showStats' => '{boolean}',
'opinionMining' => '{boolean}',
'stringIndexType' => '{string}',
);
$url->setQueryVariables($parameters);
$request->setMethod(HTTP_Request2::METHOD_POST);
// Request body
$request->setBody("{body}");
try
{
$response = $request->send();
echo $response->getBody();
}
catch (HttpException $ex)
{
echo $ex;
}
?>########### Python 2.7 #############
import httplib, urllib, base64
headers = {
# Request headers
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.urlencode({
# Request parameters
'model-version': '{string}',
'showStats': '{boolean}',
'opinionMining': '{boolean}',
'stringIndexType': '{string}',
})
try:
conn = httplib.HTTPSConnection('*.cognitiveservices.azure.us')
conn.request("POST", "/text/analytics/v3.1-preview.1/sentiment?%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################
########### Python 3.2 #############
import http.client, urllib.request, urllib.parse, urllib.error, base64
headers = {
# Request headers
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.parse.urlencode({
# Request parameters
'model-version': '{string}',
'showStats': '{boolean}',
'opinionMining': '{boolean}',
'stringIndexType': '{string}',
})
try:
conn = http.client.HTTPSConnection('*.cognitiveservices.azure.us')
conn.request("POST", "/text/analytics/v3.1-preview.1/sentiment?%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################require 'net/http'
uri = URI('https://*.cognitiveservices.azure.us/text/analytics/v3.1-preview.1/sentiment')
uri.query = URI.encode_www_form({
# Request parameters
'model-version' => '{string}',
'showStats' => '{boolean}',
'opinionMining' => '{boolean}',
'stringIndexType' => '{string}'
})
request = Net::HTTP::Post.new(uri.request_uri)
# Request headers
request['Content-Type'] = 'application/json'
# Request headers
request['Ocp-Apim-Subscription-Key'] = '{subscription key}'
# Request body
request.body = "{body}"
response = Net::HTTP.start(uri.host, uri.port, :use_ssl => uri.scheme == 'https') do |http|
http.request(request)
end
puts response.body