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            Sentiment Analysis
            API
            
           
           
                      This is a API that analyzes a hotel review sentence. It returns the features detected, and the positive or negative sentiment for each feature. 
           For example "Room was large, but water pressure was poor." , it detects that the word "Room" was carrying a positive sentiment, caused by the adjective "large", and the "water pressure"  is carrying a negative sentiment affected by the adjective "poor".
           
           
           
            
               
                    
               
                
                Endpoint: 
                         http://www.HotelSearchFreeText.com/api/Sentiment
                Method:  POST
                Request:   review sentence 
                Request format: application/json
                Response:  sentiment  response object
                Response format:  
                json object
                    
             
            
           
           
               
                   Sample Request: 
                   
                   
                   
                   curl -X POST \
                   
                          http://www.HotelSearchFreeText.com/api/Sentiment \
                            
                          -H 'cache-control: no-cache' \
                   
                          -H 'content-type: application/json' \
                   
                          -d '"room was large, but water pressure was poor"'
                
                
                Response: 
                   
                
                
                    { "sentence": "room was large, but water pressure was poor",
   "lAnalyzedWords": [
   "sentence": "room was large, but water pressure was poor",
   "lAnalyzedWords": [ {
      { "wordVector": "room",
         "sentimentValue": 1,
         "affectedBy": "large"
      },
      {
         "wordVector": "room",
         "sentimentValue": 1,
         "affectedBy": "large"
      },
      { "wordVector": "was",
         "sentimentValue": 0,
         "affectedBy": null
      },
      {
         "wordVector": "was",
         "sentimentValue": 0,
         "affectedBy": null
      },
      { "wordVector": "large",
         "sentimentValue": 1,
         "affectedBy": null
      },
      {
         "wordVector": "large",
         "sentimentValue": 1,
         "affectedBy": null
      },
      { "wordVector": ",",
         "sentimentValue": 0,
         "affectedBy": null
      },
      {
         "wordVector": ",",
         "sentimentValue": 0,
         "affectedBy": null
      },
      { "wordVector": "but",
         "sentimentValue": 0,
         "affectedBy": null
      },
      {
         "wordVector": "but",
         "sentimentValue": 0,
         "affectedBy": null
      },
      { "wordVector": "water pressure",
         "sentimentValue": -1,
         "affectedBy": "poor"
      },
      {
         "wordVector": "water pressure",
         "sentimentValue": -1,
         "affectedBy": "poor"
      },
      { "wordVector": "was",
         "sentimentValue": 0,
         "affectedBy": null
      },
      {
         "wordVector": "was",
         "sentimentValue": 0,
         "affectedBy": null
      },
      { "wordVector": "poor",
         "sentimentValue": -1,
         "affectedBy": null
      }
   ]
}
         "wordVector": "poor",
         "sentimentValue": -1,
         "affectedBy": null
      }
   ]
}