New Report - The emerging green skills gap & Gen Z attitudes

AI-driven Zero Carbon news tracker

Our AI-driven news tracker monitors the Internet for relevant news and articles constantly, updating every hour. We perform sentiment analysis and implement natural language understanding techniques to help categorise and analyse content at scale. Feel free to browse the interactive dashboards below and uncover content, trends and analysis that you may well not have know existed.

Automated systems sift the web for subject matter specific news, blogs and content

Our proprietary machine learning algorithms classify the content

Natural Language Understanding enriches, evaluates sentiment and adds structure to unstructured data

We use innovative visualisations and searches to help interrogate the data in detail

You discover content, relationships, data and trends in new and innovative ways

Key terms and links

Below you will find some key terms and useful links that help to explain how we produce the Zero Carbon Academy news tracker.

1. Natural Language Processing (NLP): Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day, from translation software, chatbots, and spam filters. Search engines, grammar correction software, voice assistants, and social media/news monitoring tools, such as the Zero Carbon Academy news tracker, leverage it—read more.
2. Sentiment: Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help monitor different scenarios, from brand and product sentiment to customer feedback and our news tracker’s news and category sentiment, usually expressed as a range between -1 (negative) and +1 (positive) or as a percentage—read more.
3. Named Entities: Named entity recognition (NER), also known as entity identification or entity extraction ‒ is a natural language processing (NLP) technique that automatically identifies named entities in a text and classifies them into predefined categories. Entities can be names of people, organisations, locations, times, quantities, monetary values, percentages, and more—read more.
4. Text Classification: This machine learning technique assigns predefined categories to open-ended text. Text classifiers can be used to organise, structure, and categorise most text – from documents, medical studies and files, and all over the web—read more.
5. Topic Analysis: Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics. The topics are not predefined and can develop over time as algorithms mature—read more.
6. Keyword Extraction: Keyword extraction (also known as keyword detection or keyword analysis) is a text analysis technique that automatically extracts the most used and most important words and expressions from a text. It helps summarise the content of texts and recognise the main topics discussed—read more.