MS Text Analytics Cognitive Service: how to work with local database? - azure

Question

Microsoft provides a service to analyze text data called Text Analytics Cognitive Service.
Is it possible to use this service with local database? i.e. not in Azure
I work with some large databases and as for me it can be interesting to use it for:
Language detection
Key phrase extraction
Named Entity recognition
Sentiment analysis

Answer 1

Once you pull your data that you would like to detect its language
from your local database, you just need to fetch it then just pass in
below method. It would analysis your value in response.
API Access Keys:
private static readonly string endpointKey = "YourEndPonitKey";
private static readonly string endpoint = "https://YourServiceURL.cognitiveservices.azure.com/text/analytics/v2.1/languages";
Code Snippet:
public async Task<object> DetectLanguageAsync(string InputFromDbOrUser)
{
try
{
DetectedLanguageResponseModel ObjDitectedLanguageResponse = new DetectedLanguageResponseModel();
//Create laguage detection request param
RequestModel objRequestModel = new RequestModel();
objRequestModel.id = "1";
objRequestModel.text = InputFromDbOrUser;
//Made Document List
List<RequestModel> Objdocuments = new List<RequestModel>();
Objdocuments.Add(objRequestModel);
//Bind Request Model
LanguageDetection objRequestList = new LanguageDetection();
objRequestList.documents = Objdocuments;
// Bind and Serialize Request Object
var serializedObject = JsonConvert.SerializeObject(objRequestList);
// Call Language Detection API
using (var client = new HttpClient())
using (var request = new HttpRequestMessage())
{
request.Method = HttpMethod.Post;
request.RequestUri = new Uri(endpoint);
request.Content = new StringContent(serializedObject, Encoding.UTF8, "application/json");
request.Headers.Add("Ocp-Apim-Subscription-Key", endpointKey);
var response = await client.SendAsync(request);
//Check status code and retrive response
if (response.IsSuccessStatusCode)
{
ResponseModel objResponse = JsonConvert.DeserializeObject<ResponseModel>(await response.Content.ReadAsStringAsync());
//Check Response List
foreach (var item in objResponse.documents)
{
//Checkings Empty Response and Return to Caller
if (objResponse.documents != null)
{
ObjDitectedLanguageResponse.Language = objResponse.documents[0].detectedLanguages[0].name;
return ObjDitectedLanguageResponse;
}
else
{
return "Sorry, I am not able to find a related topic! Would you like me to Bing Search?";
}
}
}
else
{
var result_string = await response.Content.ReadAsStringAsync();
return result_string;
}
}
return ObjDitectedLanguageResponse;
}
catch (Exception ex)
{
throw new NotImplementedException(ex.Message, ex.InnerException);
}
}
Class Used:
public class DetectedLanguage
{
public string name { get; set; }
public string iso6391Name { get; set; }
}
public class DetectedLanguageResponseModel
{
public dynamic Language { get; set; }
}
public class LanguageDetection
{
public List<RequestModel> documents { get; set; }
}
public class RequestModel
{
public string id { get; set; }
public string text { get; set; }
}
public class ResponseDocument
{
public string id { get; set; }
public List<DetectedLanguage> detectedLanguages { get; set; }
}
public class ResponseModel
{
public List<ResponseDocument> documents { get; set; }
public List<object> errors { get; set; }
}
Note: The current limit is 5,120 characters for each document; if you need to analyze larger documents, you can break them up into
smaller chunks for more you could refer official document
Hope that would help. If you need more implementation assistance please have a look on here