An API to use Neticle Technologies' semantic and text analysis functions in a standard way.

Compared to v1.1:

• Ability to send multiple synonyms for the analyzed text
• Result comes back from the API as an array of result objects.

Compared to v1.0:

• API accepts url encoded input_text via a POST request (input_text must be url encoded if sent via POST)

Doing automated semantic analysis on input text.

• Entity oriented sentiment analysis: in a text only the phrases, entities and labels related to the set target entity are analyzed. The target is set by it's synonyms, spelling and mispellings.

• Document level sentiment analysis: in a text every phrases, entities and labels are analyzed. In this case no synonyms are given as input parameter.

• Attribute recognition: service and product attributes (for example: screen, bandwidth, etc.) are recognized.

• Topic recognition: key topics (for example: 3G, mobile payment, etc.) are recognized.

• Location recognition: related locations (for example: Hungary, Pécs, etc.) are recognized.

• Brand recognition: related brands (for example: Audi, Mercedes, etc.) are recognized.

• Emotion recognition: related emotions (for example: joy, etc.) are recognized.

• Person recognition: related persons (for example: Bill Gates, etc.) are recognized.

• Organization recognition: related organizations (for example: UNICEF, etc.) are recognized.

• Event recognition: related events (for example: festivals, conferences, etc.) are recognized.

• Business topic recognition: related business topics (for example: revenue, IPO, etc.) are recognized.

• Legal topic recognition: related legal topics (for example: lawsuit, legislation, etc.) are recognized.

• Medical topic recognition: related mediacal topics (for example: receipt, sympton, etc.) are recognized.

• HR topic recognition: related HR topics (for example: job, salary, etc.) are recognized.

https://semanticapi.neticle.hu/1.2/text_analysis

https://semanticapi.neticle.hu/1.2/text_analysis?lang=hu&token=demokey2&stem=0&input_text=tapasztalataim szerint, ez egy rossz autó&format=json&synonyms=[[],["autó","Autó"]]

{
  "input_length": 41,
  "results": [
    {
      "input_length": 41,
      "recognized_negative_phrases": [
        {
          "related_pos_phrases": [
            
          ],
          "phrase": "egy rossz",
          "mention_number": 1,
          "related_entities": [
            
          ],
          "mentions": [
            " ez egy rossz autó"
          ],
          "related_neg_phrases": [
            
          ]
        }
      ],
      "keyword_stats": {
        "total_keyword_hit_number": 0,
        "total_synonym_hit_numbers": [
          
        ]
      },
      "processing_time_in_ms": 100,
      "recognized_synonyms": [
        
      ],
      "recognized_positive_phrases": [
        
      ],
      "html_formatted_text": "<span class=\"contain_keyword\">tapasztalataim szerint<\/span>, <span class=\"contain_keyword\">ez <span class=\"phrase_neg_lvl2 polarity_item\" title=\"-2 \">egy rossz<\/span> autó<\/span>",
      "opinion_index": -2.0,
      "call_id": "",
      "entities": [
        {
          "related_pos_phrases": [
            
          ],
          "entity_name": "tapasztalat",
          "entity_opinion_index": 0.0,
          "mention_number": 1,
          "related_entities": [
            
          ],
          "entity_type": "topic",
          "mentions": [
            "tapasztalataim szerint"
          ],
          "related_neg_phrases": [
            
          ]
        }
      ]
    },
    {
      "input_length": 41,
      "recognized_negative_phrases": [
        {
          "related_pos_phrases": [
            
          ],
          "phrase": "egy rossz",
          "mention_number": 1,
          "related_entities": [
            
          ],
          "mentions": [
            " ez egy rossz autó"
          ],
          "related_neg_phrases": [
            
          ]
        }
      ],
      "keyword_stats": {
        "total_keyword_hit_number": 1,
        "total_synonym_hit_numbers": [
          {
            "autó": 1
          }
        ]
      },
      "processing_time_in_ms": 80,
      "recognized_synonyms": [
        "autó"
      ],
      "recognized_positive_phrases": [
        
      ],
      "html_formatted_text": "tapasztalataim szerint, <span class=\"contain_keyword\">ez <span class=\"phrase_neg_lvl2 polarity_item\" title=\"-2 \">egy rossz<\/span> <span class=\"synonym\">autó<\/span><\/span>",
      "opinion_index": -2.0,
      "call_id": "",
      "entities": [
        
      ]
    }
  ],
  "total_processing_time_in_ms": 181
}  

It predicts the gender based on a name String value.

Possible values:
-1 : female
1 : male
0 : unknown

https://semanticapi.neticle.hu/1.1/get_gender_from_name

https://semanticapi.neticle.hu/1.0/get_gender_from_name?lang=hu&token=demokey2&name=Lajos&format=json

{
    "user_key": "demokey2",
    "processing_time_in_ms": 400,
    "gender": 0
}

It checks the language of the input text.

Possible values:
true
false

https://semanticapi.neticle.hu/1.1/lang_check

https://semanticapi.neticle.hu/1.1/lang_check?lang=hu&token=demokey2&input_text=Lajos egy új autót vett&format=json

{
    "user_key": "demokey2",
    "processing_time_in_ms": 400,
    "lang": "hu",
    "lang_check": true
}