Investigating Corporate Social Responsibility Initiatives: Examining the case of corporate Covid-19 response

TL;DR

This paper explores how NLP techniques like LDA, TextRank, and summarization can help policymakers analyze large volumes of corporate press releases during Covid-19 to understand corporate responses and inform decision-making.

Contribution

It demonstrates the application of multiple NLP methods to analyze corporate Covid-19 responses, providing a replicable approach for extracting insights from extensive textual data.

Findings

Effective summarization of corporate responses during Covid-19

Identification of key themes in corporate communications

Framework for policymakers to analyze large document sets

Abstract

In todays age of freely available information, policy makers have to take into account a huge amount of information while making decisions affecting relevant stakeholders. While increase in the amount of information sources and documents increases credibility of decisions based on the corpus of available text, it is challenging for policymakers to make sense of this information. This paper demonstrates how policy makers can implement some of the most popular topic recognition methods, Latent Dirichlet Allocation, Deep Distributed Representation method, text summarization approaches, Word Based Sentence Ranking method and TextRank for sentence extraction method, to sum up the content of large volume of documents to understand the gist of the overload of information. We have applied popular NLP methods to corporate press releases during the early period and advanced period of Covid-19 pandemic which has resulted in a global unprecedented health and socio-economic crisis, when policymaking and regulations have become especially important to standardize corporate practices for employee and social welfare in the face of similar future unseen crises. The steps undertaken in this study can be replicated to yield insights from relevant documents in any other social decision-making context.