Bitvore Cellenus for Environmental, Social and Governance (ESG)

Bitvore Cellenus for Environmental, Social and Governance (ESG) is a dataset providing ESG insights on over 400k+ global public and private companies, markets and industries from a diverse set unstructured (text) data (over 60K sources including including news, press releases, SEC filings/proxy statements, earnings call transcripts and more), both public and licensed premium content. Bitvore Cellenus leverages NLP models to identify material ESG company events along with ESG sentiment scores to identify how a company is performing across over 41 ESG related topics. These topics are aligned with industry standards such as the SASB Materiality Map and the Big 4 ESG Metrics Definitions. Sentiment scores can be trended and calculated for a company and rolled up to industry level for benchmark comparisons. ML models are used for aggregation and deduplication, similarity clustering, geolocation tagging, entity tagging (includes companies, markets, security-level IDs, etc.), ESG event detection, sentiment analysis models, scoring algorithms and more. This dataset identifies over 40 ESG material events/topics, with content level sentiment scores which can be reconfigured to customizable entity level sentiment, risk and growth scores. Security identifiers mapped include ISIN, CUSIP, SEDOL, FIGI and Exchange Ticker.

搜集汇总

背景与挑战

背景概述

Bitvore Cellenus for ESG是一个数据集，通过分析超过6万个非结构化文本源，为全球超过40万家上市公司和私营公司提供ESG洞察。它利用NLP和机器学习模型识别超过40个ESG相关主题的事件和情感得分，这些得分可自定义并汇总至行业级别进行基准比较。数据集还包含多种安全标识符映射，如ISIN和CUSIP。