Start analyze using TQL
Create a granular dataset in a secure web server for multiple Entities defined with a TQL query
This route allows a client to launch an analysis for data relevant to a list of entities using Textreveal query language (TQL).
The TextReveal Query Language (TQL) is a simple text-based query language for filtering data. It is composed with a field on which a value is applied: e.g, site_type: "news". Each filter can be combined to create a boolean expression with AND, OR and NOT operators. 
 Example: (text:"Apple TV" OR title:"Steve Jobs") AND NOT text:"apple tree" 
Unlike the dataset route, the TQL route requests all types of sites. The news site type groups news, premium_news and licensed_news. Moreover, the workers are implicitly enabled if the associated parameter is used. For example, the quality score worker will be enabled if the qscore parameter is used.
Request
Request Body
conceptsobjectA dictionary containing the concepts with the concept as key and list of keywords as value. Commas are not allowed inside.
concepts_filterobjectSame as
concepts, but also used as filters.end_date*dateThe date when the analysis should end.
Example: "2019-02-01"entities*object[]languagestring (enum)Language used for analysis (only one language allowed with TQL query)
Default: "english"Values: "english", "french", "italian", "german", "spanish", "portuguese", "romanian", "russian", "finnish", "danish", "norwegian", "czech", "slovak", "polish", "swedish", "dutch", "japanese", "chinese"min_matchnumberAt least min_match given keywords should be present in the resulted text.
Default: 1Example: 1min_repeatnumberThe minimum number of time a keyword should be present in a text.
Default: 1Example: 1qscorefloatQuality score number.
Range: [0, 100]sentiments_filterobjectFilter documents based on sentiment.
similarity_thresholdfloatSimilarity score threshold for recognized or matched entities. Filters out documents containing entities with a similarity score lesser than the threshold.
Range: [0, 1]start_date*dateThe date when the analysis should start.
Example: "2019-01-31"
{
  "concepts": {
    "environment": [
      "environmental impact",
      "environmental controversy",
      "pesticide"
    ],
    "governance": [
      "offshore transaction",
      "dupery",
      "humbug"
    ],
    "pollution": [
      "fuel leakage",
      "greenhouse gases"
    ],
    "social": [
      "unscrupulous",
      "inequality",
      "malfeasance",
      "workplace violence"
    ]
  },
  "end_date": "2019-02-01",
  "entities": [
    {
      "annotate_keywords": [
        "Apple Inc.",
        "Steve Wozniak",
        "Apple Computer",
        "Ron Wayne",
        "AC Wellness",
        "FileMaker",
        "Braeburn Capital",
        "David Pakman",
        "AAPL",
        "Apple",
        "Steve Jobs",
        "apple.com"
      ],
      "context": "Apple is a technology company that designs, manufactures, and markets consumer electronics, personal computers, and software.",
      "entity_of_interest": "apple",
      "query": "((title:\"Apple Inc.\" AND text:\"Apple Inc.\") OR (title:\"Apple\" AND text:\"Apple\")) AND ner:\"Apple\""
    }
  ],
  "language": "english",
  "min_match": 2,
  "qscore": 90,
  "sentiments_filter": {
    "positive": {
      "min": 0.5
    }
  },
  "similarity_threshold": 0.5,
  "start_date": "2019-01-31"
}