• Sentiment Analysis

    Sentiment Analysis

    Determines the polarity of any text content: negative (neg), neutral (neu), positive (pos). Supported Languages Currently supporting the following languages (14): Arabic, Chinese, English, Farsi, French, German, Hungarian, Italian, Japanese, Polish, Portuguese, Romanian, Russian, Spanish. Usage Example This is an example of calling the Sentiment Analyzer on an English text […]

  • Automatic Classification

    Automatic Classification

    Automatic Classifier IPTC Automatic classification of documents using standard IPTC (The International Press Telecommunications Council – the Global Standards Body of the News Media) taxonomy. Classification based on custom taxonomies (patents, cyber security, military intelligence or others) can be created on demand. Taxonomy The out-of-the-box IPTC classifier is trained to […]

  • Named Entity Recognition

    Named Entity Recognition

    Named entities, categorized by type (people, organizations, locations, products, etc.), constitute the core factual information of any content. These engines extract named entities out of given text. All flavors described below are supporting the same list of entity types, see the following section. Supported Entity Types We are currently supporting […]

  • Analyzing Telegram with Factory AI

    Analyzing Telegram with Factory AI

    IntelliDockers , part of AI Factory, is a powerful adaptable and multilingual Text Analytics solution that can perform dozens of AI NLP tasks. Some of these can be used to extract information from social media or instant messaging platforms like Telegram Messenger, that do now allow programmatic (API-based) access to their content. One way to crawl content […]

  • TrustServista whitepaper

    TrustServista whitepaper

    Automated Approaches to Digital Content Verification

  • AI Factory datasheet

    AI Factory datasheet

    Download the product datasheet for Zetta Cloud – AI Factory.

  • How to build high-quality Classifiers with limited training data

    How to build high-quality Classifiers with limited training data

    Automate Classifiers are the bread and butter of automated content understanding. They help categorize documents against any desired taxonomy, from IPTC Newscodes to sentiment, intent, stance, or emotion. Building your own classifier with Factory no-code AI is as easy as it gets (and secure, too!), but what happens if you […]