Retrieve a payload
Retrieve the payload of an analysis previously run.
Request
Response
Get analysis by id response. The payload will be in the file attachment of the response.
co_mentionsstring[]List of keywords to search with the keywords list. Works like a boolean
AND. Example:keywords : ["TotalEnergy"]co_mentions: ["gas", "oil price"]
Behavior: TextReveal® API will look for documents relevant to at least one of the co_mentions. For the above example, below are the different cases of relevancy:
TotalEnergyandgasTotalEnergyandoil priceTotalEnergyandoil priceandgas
N.B: Search of
Example: ["tablets"]co_mentionsis operated in full-text and is case insensitive.conceptsobjectList of concepts or risks that are to be analyzed. Each individual concept is defined by its own list of keywords.
Punctuation is not handled in the concept labels. Each concept label must be unique (case insensitive).
concepts_filterobjectSame as
conceptsbut filters out documents that does not contain the concepts.Note: You can either use
conceptsorconcepts_filtercountriesstring[]List of countries to search (field
thread.country).N.B: Use
Example: ["US"]alpha-2format.end_date*dateThe date when the anaysis should end.
Example: "2019-02-01"entities*object[]keywords_excludestring[]List of keywords to exclude from the search. Works like a boolean
AND NOT.Example:
keywords: ["apple", "iphone"]keywords_exclude: ["Steve Jobs", "Tim Cook"]Behavior: TextReveal® API will look for documents relevant toapplethe company orIphonebut NOT containing eitherSteve JobsorTim Cook.
N.B: Search of
Example: ["Steve Jobs"]keywords_excludeis operated in full-text and is case insensitive.languages(string (enum))[]List of languages to search, see Language Support page for more information.
Note: We do not recommend using multiple values.
Default: ["english"]Values: "english", "french", "italian", "german", "spanish", "portuguese", "romanian", "russian", "finnish", "danish", "norwegian", "czech", "slovak", "polish", "swedish", "dutch", "japanese", "chinese"min_matchnumberThe message must contain at least
min_matchkeywords.When used, each entity must have at least
min_matchkeywords.Example:
keywords: ["apple", "iphone", "macbook"]min_match: 2
Behavior: TextReveal® API will only keep the document if and only if at least 2 elements from the keywords list appear in the document.
Default: 1Example: 1min_repeatnumberThe message must contain at least
min_repeatoccurrence of a keyword.Example:
keywords: ["apple", "iphone"]min_repeat: 2
Behavior: TextReveal® API will only keep the document if and only if it contains at least 2 occurrences of either
Default: 1Example: 1appleoriphone.qscorefloatQuality threshold to filter out unreadable data.
No filtering is applied if the
Default: 50Range: [0, 100]quality-scoreworker is not provided.search_instring[]Allows to define if the documents extraction has to be done by searching entity keywords in the title and/or in the text.
Example:
search_in: ["title", "text"]
Note:
- This parameter is only applied on the keywords of entity, not on 
keywords_exclude,co_mentions,neg_keywords,min_repeat,min_match. - Not available with 
ner-linkingworker. 
sentiments_filterobjectPartial object containing a min/max values for each sentiments. The end analysis will contains documents that match these filters.
Note: This can be compared to a filter on:
document_{sentiment}.meankey forpositive,negativeandneutraldocument_{sentiment}key forpolarity
similarity_thresholdfloatSimilarity score threshold for recognized or matched entities. Filters out documents containing entities with a similarity score lesser than the threshold.
Default: 0Range: [0, 1]site_type(string (enum))[]Type of sites to search (field
Default: ["news", "blogs", "discussions"]Values: "news", "blogs", "discussions", "licensed_news", "premium_news"thread.site_type)sitesstring[]List of websites to search.
N.B: Use the base domain of the websites.
Example: ["apple.com"]sites_excludestring[]A list of source sites to be excluded.
Example: ["apple.com"]start_date*dateThe date when the analysis should start.
Example: "2019-01-31"workers*(string (enum))[]List of the tasks that will be used for analysis. Should contain at least 'raw-matcher' or 'ner-linking'.
Values: "quality-score", "ner-linking", "raw-matcher", "concept", "entity-similarity", "embedder-indicators"
{
  "concepts": {
    "environment": [
      "environmental impact",
      "environmental controversy",
      "pesticide"
    ],
    "governance": [
      "offshore transaction",
      "dupery",
      "humbug"
    ],
    "pollution": [
      "fuel leakage",
      "greenhouse gases"
    ],
    "social": [
      "unscrupulous",
      "inequality",
      "malfeasance",
      "workplace violence"
    ]
  },
  "countries": [
    "US",
    "FR"
  ],
  "end_date": "2019-02-01",
  "entities": [
    {
      "context": "Apple is a technology company that designs, manufactures, and markets consumer electronics, personal computers, and software.",
      "entity_of_interest": "apple",
      "keywords": [
        "Apple Inc.",
        "Steve Wozniak",
        "Apple Computer",
        "Ron Wayne",
        "AC Wellness",
        "FileMaker",
        "Braeburn Capital",
        "David Pakman",
        "AAPL",
        "Apple",
        "Steve Jobs",
        "apple.com"
      ]
    },
    {
      "context": "SESAMm is a fintech company that specializes in big data and artificial intelligence for investment.",
      "entity_of_interest": "sesamm",
      "keywords": [
        "SESAMm SAS",
        "Florian Aubry",
        "SESAMm",
        "Pierre Rinaldi",
        "Sylvain Forté",
        "sesamm.com"
      ]
    }
  ],
  "languages": [
    "english"
  ],
  "qscore": 90,
  "sentiments_filter": {
    "positive": {
      "min": 0.5
    }
  },
  "similarity_threshold": 0.5,
  "site_type": [
    "news",
    "blogs",
    "discussions"
  ],
  "sites_exclude": [
    "apple.com"
  ],
  "start_date": "2019-01-31",
  "workers": [
    "quality-score",
    "concept",
    "raw-matcher",
    "ner-linking",
    "entity-similarity",
    "embedder-indicators"
  ]
}