Client library for api.datumbox.com
npm install --save @datafire/datumbox
let datumbox = require('@datafire/datumbox').create();
.then(data => {
console.log(data);
});
Datumbox offers a Machine Learning platform composed of 14 classifiers and Natural Language processing functions. Functions include sentiment analysis, topic classification, readability assessment, language detection, and much more.
The Adult Content Detection function classifies the documents as adult or noadult based on their context. It can be used to detect whether a document contains content unsuitable for minors.
datumbox.AdultContentDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Commercial Detection function labels the documents as commercial or non-commercial based on their keywords and expressions. It can be used to detect whether a website is commercial or not.
datumbox.CommercialDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Document Similarity function estimates the degree of similarity between two documents. It can be used to detect duplicate webpages or detect plagiarism.
datumbox.DocumentSimilarity({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - copy
string
: The second text. It should not contain HTML tags. - original
string
: The first text. It should not contain HTML tags.
- api_key required
Output schema unknown
The Educational Detection function classifies the documents as educational or non-educational based on their context. It can be used to detect whether a website is educational or not.
datumbox.EducationalDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Gender Detection function identifies if a particular document is written-by or targets-to a man or a woman based on the context, the words and the idioms found in the text.
datumbox.GenderDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Keyword Extraction function enables you to extract from an arbitrary document all the keywords and word-combinations along with their occurrences in the text.
datumbox.KeywordExtraction({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - n
integer
: The number of keyword combinations (n-grams) that you wish to extract. - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Language Detection function identifies the natural language of the given document based on its words and context. This classifier is able to detect 96 different languages.
datumbox.LanguageDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Readability Assessment function determines the degree of readability of a document based on its terms and idioms. The texts are classified as basic, intermediate and advanced depending their difficulty.
datumbox.ReadabilityAssessment({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Sentiment Analysis function classifies documents as positive, negative or neutral (lack of sentiment) depending on whether they express a positive, negative or neutral opinion.
datumbox.SentimentAnalysis({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Spam Detection function labels documents as spam or nospam by taking into account their context. It can be used to filter out spam emails and comments.
datumbox.SpamDetection({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Subjectivity Analysis function categorizes documents as subjective or objective based on their writing style. Texts that express personal opinions are labeled as subjective and the others as objective.
datumbox.SubjectivityAnalysis({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Text Extraction function enables you to extract the important information from a given webpage. Extracting the clear text of the documents is an important step before any other analysis.
datumbox.TextExtraction({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The HTML source of the webpage.
- api_key required
Output schema unknown
The Topic Classification function assigns documents in 12 thematic categories based on their keywords, idioms and jargon. It can be used to identify the topic of the texts.
datumbox.TopicClassification({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text that you want to analyze. It should not contain HTML tags.
- api_key required
Output schema unknown
The Twitter Sentiment Analysis function allows you to perform Sentiment Analysis on Twitter. It classifies the tweets as positive, negative or neutral depending on their context.
datumbox.TwitterSentimentAnalysis({
"api_key": ""
}, context)
- input
object
- api_key required
string
: Your API Key - text
string
: The text of the tweet that we evaluate.
- api_key required
Output schema unknown
This integration has no definitions