Environmental Social Governance (ESG) is a widely used metric that measures
the sustainability of a company practices. Currently, ESG is determined using
self-reported corporate filings, which allows companies to portray themselves
in an artificially positive light. As a result, ESG evaluation is subjective
and inconsistent across raters, giving executives mixed signals on what to
improve. This project aims to create a data-driven ESG evaluation system that
can provide better guidance and more systemized scores by incorporating social
sentiment. Social sentiment allows for more balanced perspectives which
directly highlight public opinion, helping companies create more focused and
impactful initiatives. To build this, Python web scrapers were developed to
collect data from Wikipedia, Twitter, LinkedIn, and Google News for the S&P 500
companies. Data was then cleaned and passed through NLP algorithms to obtain
sentiment scores for ESG subcategories. Using these features, machine-learning
algorithms were trained and calibrated to S&P Global ESG Ratings to test their
predictive capabilities. The Random-Forest model was the strongest model with a
mean absolute error of 13.4% and a correlation of 26.1% (p-value 0.0372),
showing encouraging results. Overall, measuring ESG social sentiment across
sub-categories can help executives focus efforts on areas people care about
most. Furthermore, this data-driven methodology can provide ratings for
companies without coverage, allowing more socially responsible firms to thrive.

该研究论文通过使用社会情感考量，旨在创建一个数据驱动的 ESG 评估体系，该体系可以通过提供更加平衡的观点和更系统化的评分来为公司提供更好的指导，从而帮助公司创建更具针对性和有影响力的倡议。使用基于 Python 的网络爬虫从维基百科、推特、领英和谷歌新闻中收集数据，然后通过自然语言处理算法对 ESG 子类别进行情感评分。然后使用这些特征，训练和校准机器学习算法以测试其预测能力，其中最强大的模型是随机森林模型，其平均绝对误差为 13.4％，相关性为 26.1％（p 值为 0.0372），显示出令人鼓舞的结果。总的来说，跨子类别测量 ESG 社会情感可以帮助高管将努力集中在人们最关注的领域。此外，这种数据驱动方法可以为没有覆盖范围的公司提供评级，从而使更多具有社会责任感的公司得以发展。

应用社交网络分析和机器学习创建系统化的 ESG 评分体系以推动更可持续的公司运营实践

Creating a Systematic ESG (Environmental Social Governance) Scoring  System Using Social Network Analysis and Machine Learning for More  Sustainable Company Practices

It is a challenging task to predict financial markets. The complexity of this
task is mainly due to the interaction between financial markets and market
participants, who are not able to keep rational all the time, and often
affected by emotions such as fear and ecstasy. Based on the state-of-the-art
approach particularly for financial market predictions, a hybrid convolutional
LSTM Based variational sequence-to-sequence model with attention (CLVSA), we
propose a novel deep learning approach, named dual-CLVSA, to predict financial
market movement with both trading data and the corresponding social sentiment
measurements, each through a separate sequence-to-sequence channel. We evaluate
the performance of our approach with backtesting on historical trading data of
SPDR SP 500 Trust ETF over eight years. The experiment results show that
dual-CLVSA can effectively fuse the two types of data, and verify that
sentiment measurements are not only informative for financial market
predictions, but they also contain extra profitable features to boost the
performance of our predicting system.

这篇论文提出了一种基于深度学习和序列到序列模型的新方法，名为 dual-CLVSA，可通过两个独立的渠道（交易数据和社会情感测量）来预测金融市场走势，并验证了社会情感测量不仅对于金融市场预测是信息丰富的，而且也包含额外的有效特征，以提高我们的预测系统的表现。该方法在 SPDR SP 500 Trust ETF 的历史交易数据上进行回测实验证明了其有效性。