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cloudmersive_nlp_api_client.AnalyticsApi

All URIs are relative to https://api.cloudmersive.com

Method HTTP request Description
analytics_hate_speech POST /nlp-v2/analytics/hate-speech Perform Hate Speech Analysis and Detection on Text
analytics_profanity POST /nlp-v2/analytics/profanity Perform Profanity and Obscene Language Analysis and Detection on Text
analytics_sentiment POST /nlp-v2/analytics/sentiment Perform Sentiment Analysis and Classification on Text
analytics_similarity POST /nlp-v2/analytics/similarity Perform Semantic Similarity Comparison of Two Strings
analytics_subjectivity POST /nlp-v2/analytics/subjectivity Perform Subjectivity and Objectivity Analysis on Text

analytics_hate_speech

HateSpeechAnalysisResponse analytics_hate_speech(input)

Perform Hate Speech Analysis and Detection on Text

Analyze input text using advanced Hate Speech Analysis to determine if the input contains hate speech language. Supports English language input. Consumes 1-2 API calls per sentence.

Example

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'

# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.AnalyticsApi(cloudmersive_nlp_api_client.ApiClient(configuration))
input = cloudmersive_nlp_api_client.HateSpeechAnalysisRequest() # HateSpeechAnalysisRequest | Input hate speech analysis request

try:
    # Perform Hate Speech Analysis and Detection on Text
    api_response = api_instance.analytics_hate_speech(input)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling AnalyticsApi->analytics_hate_speech: %s\n" % e)

Parameters

Name Type Description Notes
input HateSpeechAnalysisRequest Input hate speech analysis request

Return type

HateSpeechAnalysisResponse

Authorization

Apikey

HTTP request headers

  • Content-Type: application/json, text/json, application/xml, text/xml, application/x-www-form-urlencoded
  • Accept: application/json

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analytics_profanity

ProfanityAnalysisResponse analytics_profanity(input)

Perform Profanity and Obscene Language Analysis and Detection on Text

Analyze input text using advanced Profanity and Obscene Language Analysis to determine if the input contains profane language. Supports English language input. Consumes 1-2 API calls per sentence.

Example

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'

# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.AnalyticsApi(cloudmersive_nlp_api_client.ApiClient(configuration))
input = cloudmersive_nlp_api_client.ProfanityAnalysisRequest() # ProfanityAnalysisRequest | Input profanity analysis request

try:
    # Perform Profanity and Obscene Language Analysis and Detection on Text
    api_response = api_instance.analytics_profanity(input)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling AnalyticsApi->analytics_profanity: %s\n" % e)

Parameters

Name Type Description Notes
input ProfanityAnalysisRequest Input profanity analysis request

Return type

ProfanityAnalysisResponse

Authorization

Apikey

HTTP request headers

  • Content-Type: application/json, text/json, application/xml, text/xml, application/x-www-form-urlencoded
  • Accept: application/json

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analytics_sentiment

SentimentAnalysisResponse analytics_sentiment(input)

Perform Sentiment Analysis and Classification on Text

Analyze input text using advanced Sentiment Analysis to determine if the input is positive, negative, or neutral. Supports English language input. Consumes 1-2 API calls per sentence.

Example

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'

# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.AnalyticsApi(cloudmersive_nlp_api_client.ApiClient(configuration))
input = cloudmersive_nlp_api_client.SentimentAnalysisRequest() # SentimentAnalysisRequest | Input sentiment analysis request

try:
    # Perform Sentiment Analysis and Classification on Text
    api_response = api_instance.analytics_sentiment(input)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling AnalyticsApi->analytics_sentiment: %s\n" % e)

Parameters

Name Type Description Notes
input SentimentAnalysisRequest Input sentiment analysis request

Return type

SentimentAnalysisResponse

Authorization

Apikey

HTTP request headers

  • Content-Type: application/json, text/json, application/xml, text/xml, application/x-www-form-urlencoded
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]

analytics_similarity

SimilarityAnalysisResponse analytics_similarity(input)

Perform Semantic Similarity Comparison of Two Strings

Analyze two input text strings, typically sentences, and determine the semantic similarity of each. Semantic similarity refers to the degree to which two sentences mean the same thing semantically. Uses advanced Deep Learning to perform the semantic similarity comparison. Consumes 1-2 API calls per sentence.

Example

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'

# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.AnalyticsApi(cloudmersive_nlp_api_client.ApiClient(configuration))
input = cloudmersive_nlp_api_client.SimilarityAnalysisRequest() # SimilarityAnalysisRequest | Input similarity analysis request

try:
    # Perform Semantic Similarity Comparison of Two Strings
    api_response = api_instance.analytics_similarity(input)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling AnalyticsApi->analytics_similarity: %s\n" % e)

Parameters

Name Type Description Notes
input SimilarityAnalysisRequest Input similarity analysis request

Return type

SimilarityAnalysisResponse

Authorization

Apikey

HTTP request headers

  • Content-Type: application/json, text/json, application/xml, text/xml, application/x-www-form-urlencoded
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]

analytics_subjectivity

SubjectivityAnalysisResponse analytics_subjectivity(input)

Perform Subjectivity and Objectivity Analysis on Text

Analyze input text using advanced Subjectivity and Objectivity Language Analysis to determine if the input text is objective or subjective, and how much. Supports English language input. Consumes 1-2 API calls per sentence.

Example

from __future__ import print_function
import time
import cloudmersive_nlp_api_client
from cloudmersive_nlp_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_nlp_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'
# Uncomment below to setup prefix (e.g. Bearer) for API key, if needed
# configuration.api_key_prefix['Apikey'] = 'Bearer'

# create an instance of the API class
api_instance = cloudmersive_nlp_api_client.AnalyticsApi(cloudmersive_nlp_api_client.ApiClient(configuration))
input = cloudmersive_nlp_api_client.SubjectivityAnalysisRequest() # SubjectivityAnalysisRequest | Input subjectivity analysis request

try:
    # Perform Subjectivity and Objectivity Analysis on Text
    api_response = api_instance.analytics_subjectivity(input)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling AnalyticsApi->analytics_subjectivity: %s\n" % e)

Parameters

Name Type Description Notes
input SubjectivityAnalysisRequest Input subjectivity analysis request

Return type

SubjectivityAnalysisResponse

Authorization

Apikey

HTTP request headers

  • Content-Type: application/json, text/json, application/xml, text/xml, application/x-www-form-urlencoded
  • Accept: application/json

[Back to top] [Back to API list] [Back to Model list] [Back to README]