GaiaLens News Data: real-time (refreshed daily), covers c.17,000 global publicly traded companies, tracks 50 ESG themes

We can offer the news data in two formats: 1) News flow: all news flow for our company coverage including articles and tweets. 2) ESG Incidents: highlights any pressing issues that companies are facing in the news. 1) News flow --------------- Our system executes around 100,000 searches per day across the internet. We search specific websites deemed to be high-quality and informationally additive for news about our whole company coverage. These include: • Mainstream publications like Reuters, CNN, CNBC, NBC News etc. • NGO websites such as Ethical Consumer and Anti-Slavery International • Investigative journalist websites like MLex • National papers like the Japan Times • Trade publications like Insurance Journal • Sustainability publications like Edie.net Each article that we download goes through rigorous processing. This includes cleaning the body of the article and adding its metadata e.g., the date that it was published. We then calculate our proprietary “relevance” scores. This is a calculation to determine how relevant the article is to the company, CEO, biggest Insider and biggest Outsider. Natural Language Processing (NLP) techniques are used to calculate the similarity and sentiment scores for each article for each news topic. We use Twitter’s API to download the latest tweets from Thought Leader Accounts. We track over 100 Thought Leaders such as Ceres and Science Based Targets. These tweets are then searched to see if any of our company coverage is mentioned. Afterwards, the same processing and calculation steps are followed as for the news articles. 2) ESG Incidents ------------------- ESG Incidents is the second news feed that we display for users. It is designed to show any pressing issues that a company is facing in the news in real-time. To get ESG Incidents outputs we follow these steps: 1. Choose a time period of news to look at e.g., 3 months. 2. For each news topic (we have around 50) pick out the article(s) that have the highest relevance to a company and the highest similarity score over that time period. We multiply these two scores together to calculate an “Incidence Score”. 3. Calculate how many times that new topic has come up in the news over the chosen time period as a proportion of the total articles for that company. We are then able to see emerging trends and incidents for a particular company over a time period and also have the ability to see the most relevant articles for each news topic. This allows investors to see any emerging incidents or scandals for a company in real-time.

搜集汇总

背景与挑战

背景概述

该数据集提供实时每日更新的新闻数据，覆盖全球约1.7万家上市公司，追踪50个ESG主题。数据分为新闻流和ESG事件两部分：新闻流通过广泛搜索高质量来源并应用自然语言处理技术，计算相关性和情感评分；ESG事件则基于时间窗口内的相关文章识别公司面临的紧迫问题，帮助投资者实时监控新兴趋势和事件。