Retrieve a payload
Retrieve the payload of an analysis previously run.
Request
Response
Response - 200
Get analysis by id response. The payload will be in the file attachment of the response.
co_mentions
string[]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:
TotalEnergy
andgas
TotalEnergy
andoil price
TotalEnergy
andoil price
andgas
N.B: Search of
Example: ["tablets"]co_mentions
is operated in full-text and is case insensitive.concepts
objectList 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_filter
objectSame as
concepts
but filters out documents that does not contain the concepts.Note: You can either use
concepts
orconcepts_filter
countries
string[]List of countries to search (field
thread.country
).N.B: Use
Example: ["US"]alpha-2
format.end_date*
dateThe date when the anaysis should end.
Example: "2019-02-01"entities*
object[]keywords_exclude
string[]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 toapple
the company orIphone
but NOT containing eitherSteve Jobs
orTim Cook
.
N.B: Search of
Example: ["Steve Jobs"]keywords_exclude
is 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_match
numberThe message must contain at least
min_match
keywords.When used, each entity must have at least
min_match
keywords.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.
min_repeat
numberThe message must contain at least
min_repeat
occurrence 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: 1apple
oriphone
.qscore
floatQuality threshold to filter out unreadable data.
No filtering is applied if the
Default: 50Range: [0, 100]quality-score
worker is not provided.search_in
string[]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-linking
worker.
sentiments_filter
objectPartial 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}.mean
key forpositive
,negative
andneutral
document_{sentiment}
key forpolarity
similarity_threshold
floatSimilarity 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
)sites
string[]List of websites to search.<br /> N.B: Use the base domain of the websites.
Example: ["apple.com"]sites_exclude
string[]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"
]
}