Leveraging NLP approaches to define and implement text relevance hierarchy framework for business news classification

Abstract

Managing large volumes of business news data is important for companies. However, identifying relevant data can be challenging due to the dynamic nature of business environments and varying market needs. The article acknowledges this challenge and suggests that the lack of a standardized definition for relevant data makes it even more difficult for companies to prioritize and manage their data effectively, which can lead to missed opportunities. To address this challenge, the article proposes a text relevance hierarchy framework consisting of five levels that assess the relevance of business news texts to a company's specific operations and interests. The framework uses criteria such as specific topics, organizations, people, locations, and financial figures involved in the article to evaluate the importance of texts. Natural Language Processing (NLP) approaches such as entity recognition, topic modeling, and similarity analysis can be leveraged to implement the text relevancy hierarchy. By using this framework, companies can prioritize and manage their business news information efficiently, focus on the most important and relevant texts, and identify new areas of interest based on changing market needs.