| Abstrakt: |
News written in the press about different companies generates consumer feelings that can condition the reputation of these companies and, consequently, their financial results. One of the practices that might improve a company's reputation is the Environmental, Social and Governance (ESG) investment criteria. In this research, using Natural Language Processing techniques like Sentiment Analysis and Word2Vec, we detected those ESG-related terms that the written press uses in news articles about companies. Thus, we have been able to discover and analyze those terms that improve sympathy toward companies, and those that worsen it. Our findings show that those terms related to sustainable development, good social practices and ethical governance improve the general public's opinion of a company, while those related to greenwashing and socialwashing worsen it. Therefore, this methodology is valid for enabling companies to detect those terms that improve or worsen their reputation, and thus help them make decisions that improve their image. [ABSTRACT FROM AUTHOR] |
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