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. But, premium_news and licensed_news site type might not be available for your account. Contact your account manager to if this is not correct.
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.

Valid TQL fields are:

site_type: the type of site (news, blogs, discussions)
text: the text content of the document
title: the title of the document
ner: Enable ner worker and annotate a keyword

Valid TQL operators are:

AND: logical AND
OR: logical OR
NOT: logical NOT
(): grouping

POST

https://api.textreveal.com/api/2.0/analyze/tql

Request Body

conceptsobject
A dictionary containing the concepts with the concept as key and list of keywords as value. Commas are not allowed inside.
concepts_filterobject
Same as concepts, but also used as filters.
end_date*date
The 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_matchnumber
At least min_match given keywords should be present in the resulted text.
Default: 1Example: 1
min_repeatnumber
The minimum number of time a keyword should be present in a text.
Default: 1Example: 1
qscorefloat
Quality score number.
Range: [0, 100]
sentiments_filterobject
Filter documents based on sentiment.
similarity_thresholdfloat
Similarity 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*date
The date when the analysis should start.
Example: "2019-01-31"

Request

{
  "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"
}

{
  "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"
}

Response - 200

An identifier of the analysis to retrieve results

instanceuuid
The identifier of an analysis. This identifier have to be used to get results.
Example: "a62caf56-5961-4fff-ba2e-6d4dcf98960f"

Response

{
  "instance": "a62caf56-5961-4fff-ba2e-6d4dcf98960f"
}

{
  "instance": "a62caf56-5961-4fff-ba2e-6d4dcf98960f"
}