Environmental, Social, and Governance (ESG) has its own language. For investment stakeholders, this language is critical for evaluating, complying, and comparing ESG practices. It also influences how Natural Language Processing (NLP) models identify items that data systems track and analyze—concept identification and extraction.
Furthermore, this language impacts how things are clustered and classified, as they affect programs and data designs, artificial intelligence training strategies, and outputs, and dictates whether the data exploited is actually of value to the organization.
Moreover, ontologies offer ESG teams the opportunity to control the algorithms and data they use. Thus, an ontology paves the way to create resilient ESG planning models, increase productivity, and deliver effective guidance to investment strategies. Ontologies also help investors’ take a data-driven approach, expanding the boundaries and reshaping traditional patterns.
Sentiment analysis is low in complexity, requires manageable data, and fits with a large number of ESG applications. It can also support portfolio monitoring, engagement strategies, and marketing initiatives. From a technical perspective, sentiment analysis falls into the broad category of supervised learning text classification, where the model inputs a sentence and outputs a score for each sentiment class.
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