=begin #nlpapiv2 #The powerful Natural Language Processing APIs (v2) let you perform part of speech tagging, entity identification, sentence parsing, and much more to help you understand the meaning of unstructured text. OpenAPI spec version: v1 Generated by: https://github.com/swagger-api/swagger-codegen.git Swagger Codegen version: 2.3.1 =end require 'spec_helper' require 'json' # Unit tests for CloudmersiveNlpApiClient::AnalyticsApi # Automatically generated by swagger-codegen (github.com/swagger-api/swagger-codegen) # Please update as you see appropriate describe 'AnalyticsApi' do before do # run before each test @instance = CloudmersiveNlpApiClient::AnalyticsApi.new end after do # run after each test end describe 'test an instance of AnalyticsApi' do it 'should create an instance of AnalyticsApi' do expect(@instance).to be_instance_of(CloudmersiveNlpApiClient::AnalyticsApi) end end # unit tests for analytics_hate_speech # 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. # @param input Input hate speech analysis request # @param [Hash] opts the optional parameters # @return [HateSpeechAnalysisResponse] describe 'analytics_hate_speech test' do it "should work" do # assertion here. ref: https://www.relishapp.com/rspec/rspec-expectations/docs/built-in-matchers end end # unit tests for analytics_profanity # 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. # @param input Input profanity analysis request # @param [Hash] opts the optional parameters # @return [ProfanityAnalysisResponse] describe 'analytics_profanity test' do it "should work" do # assertion here. ref: https://www.relishapp.com/rspec/rspec-expectations/docs/built-in-matchers end end # unit tests for analytics_sentiment # 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. # @param input Input sentiment analysis request # @param [Hash] opts the optional parameters # @return [SentimentAnalysisResponse] describe 'analytics_sentiment test' do it "should work" do # assertion here. ref: https://www.relishapp.com/rspec/rspec-expectations/docs/built-in-matchers end end # unit tests for analytics_similarity # 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. # @param input Input similarity analysis request # @param [Hash] opts the optional parameters # @return [SimilarityAnalysisResponse] describe 'analytics_similarity test' do it "should work" do # assertion here. ref: https://www.relishapp.com/rspec/rspec-expectations/docs/built-in-matchers end end # unit tests for analytics_subjectivity # 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. # @param input Input subjectivity analysis request # @param [Hash] opts the optional parameters # @return [SubjectivityAnalysisResponse] describe 'analytics_subjectivity test' do it "should work" do # assertion here. ref: https://www.relishapp.com/rspec/rspec-expectations/docs/built-in-matchers end end end