@datafire/datumbox

Client library for api.datumbox.com

Installation and Usage

npm install --save @datafire/datumbox

let datumbox = require('@datafire/datumbox').create();

.then(data => {
  console.log(data);
});

Description

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.

Actions

AdultContentDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

CommercialDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

DocumentSimilarity

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

• 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.

Output

Output schema unknown

EducationalDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

GenderDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

KeywordExtraction

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

• 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.

Output

Output schema unknown

LanguageDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

ReadabilityAssessment

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

SentimentAnalysis

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

SpamDetection

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

SubjectivityAnalysis

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

TextExtraction

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

• input object
  • api_key required string: Your API Key
  • text string: The HTML source of the webpage.

Output

Output schema unknown

TopicClassification

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

• input object
  • api_key required string: Your API Key
  • text string: The text that you want to analyze. It should not contain HTML tags.

Output

Output schema unknown

TwitterSentimentAnalysis

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

• input object
  • api_key required string: Your API Key
  • text string: The text of the tweet that we evaluate.

Output

Output schema unknown

Definitions

This integration has no definitions