Environmental, social and governance (ESG) criteria have become essential to assess the sustainability and social impact of companies. This article presents the development of an automated ESG ranking system that uses natural language processing (NLP), sentiment analysis, and machine learning techniques to rank and rate companies based on ESG metrics. Using a pre-existing database of news articles, we used the VADER sentiment analysis tool to assess the polarity of the text data, categorizing it as positive, negative or neutral. Sentiment scores were converted to numerical scores for each ESG component. In addition, Node2Vec is integrated to create network graphs that represent the relationships and interconnections between companies, allowing a comprehensive analysis of potential impacts. The results were visualized with Altair to provide a clear view of ESG trends and relationships that impact the company’s performance over time. This study demonstrates the utility of combining NLP and advanced graph analytics for scalable data-driven ESG assessment.