Datumbox Text Analysis

Key Features

• Sentiment and Subjectivity Analysis

  Classifies documents as positive, negative, or neutral, and distinguishes between subjective opinions and objective facts.

• Topic Classification

  Assigns documents into 12 thematic categories based on keywords and jargon.

• Language Detection

  Identifies the natural language of a document, with support for 96 different languages.

• Spam and Adult Content Detection

  Labels documents as spam or non-spam and detects content that may be unsuitable for minors.

• Readability Assessment

  Determines the difficulty level of a text, classifying it as basic, intermediate, or advanced.

• Keyword and Text Extraction

  Extracts keywords, word combinations, and clear text from webpages.

• Document Similarity

  Estimates the degree of similarity between two documents to help identify duplicates or plagiarism.

• REST API

  A web service using JSON that allows developers to call machine learning functions from their applications.

Use Cases

• Social Media Monitoring

  Analyzing user opinions and sentiment to evaluate brand feedback.

• SEO Tool Development

  Extracting keywords and assessing the readability and quality of webpages.

• Community Moderation

  Filtering spam messages and inappropriate content in online forums.

• Text Classification

  Categorizing documents by topic or language for organization.

FAQ

What is Datumbox Text Analysis?

It is an open-source machine learning framework and REST API designed to provide text analysis and Natural Language Processing functions.

How much does the Datumbox API cost?

The service is free, although it limits users to 1,000 API calls per day.

Can I use Datumbox if I am not a machine learning expert?

The REST API provides off-the-shelf solutions that allow you to add machine learning components to your app without needing a theoretical background in ML.

What happens if I need more than 1,000 API calls per day?

The vendor suggests using the open-source Datumbox Machine Learning Framework instead of the API if higher call volumes are required.