AI-Powered Evaluation of Corporate Social Impact Using NLP

Abstract

Traditional methods of measuring Corporate Social Responsibility (CSR) are frequently plagued by serious shortcomings, such as over-dependence on company-reported data, absence of real-time responsiveness, and narrow representation of public opinion or third-party views. These shortcomings lead to CSR measurements that are prone to bias, static, and divorced from the social context. As business responsibility and ethical investing come to the forefront increasingly, there is a pressing need for stronger, data-based solutions that can capture an organization\'s actual social footprint. This paper presents an AI-based system for CSR impact measurement that closes these gaps by tapping into publicly available data from diverse, real-time sources like news media, social media, and government and NGO databases. Leveraging natural language processing capability and machine learning, the system extracts unstructured text data and condenses it into quantifiable CSR scores. Scores are metrics of organizational contributions to sustainability, community, and ethics. The system is designed to provide transparent, dynamic, and comparative assessment of corporate behavior to enable stakeholders to make informed decisions based on comparative evidence-based evaluation. This effort further contributes to the advancement of automatic, objective CSR measurement techniques in terms of society context and real conversation.

Introduction

The paper presents an AI-driven system, the AI-Powered Social Good Impact Evaluator, designed to improve Corporate Social Responsibility (CSR) measurement by overcoming limitations of traditional, self-reported CSR scorecards. Unlike conventional methods, which often rely on biased, outdated, or incomplete company reports, this system uses natural language processing (NLP) and machine learning to analyze diverse, publicly available data sources—such as social media, news, NGO, and government reports—for a more objective, real-time assessment of corporate social impact.

The system consists of several modules:

• Data Collection gathers data via APIs and web scraping from credible public sources.

• NLP Processing extracts company-related entities, sentiment, and topics related to CSR themes like sustainability and ethics.

• Impact Scoring Algorithm uses engineered features and machine learning regressors to generate dynamic Social Good Impact Scores reflecting a company’s real-world CSR performance.

• Visualization Dashboard offers an interactive interface to compare companies and track CSR trends by sector, region, and time.

This AI-based approach enhances transparency, objectivity, and social accountability by incorporating external public perception and independent data, thus enabling more informed and responsible decision-making by stakeholders such as investors, regulators, NGOs, and consumers.

The paper also reviews related AI and sentiment analysis research, explaining how recent advances in deep learning and large language models support improved text analysis for CSR evaluation. The system’s dashboard organizes CSR impact into categories like education, sustainability, and health, with detailed metrics across sustainability, community engagement, ethical business, and public outreach.

Conclusion

This study proposes an AI-driven system for quantifying the corporate effectiveness of companies based on publicly accessible online information and sophisticated methods for processing natural language. The proposed system derives an early image of more dynamic, more open, and faster corporate behavior from actual dialogues regarding traditional reliance on trivial CSR reports. Systematic web scraping, data preprocessing, mood analysis, entity recognition, and mentioned topic modeling allow the system to successfully extract unstructured textual information in beneficial CSR-IMPACT scores along the most important dimensions of sustainability, community commitment, and ethical behavior. The resultant scores of social goodwill provide interest groups with a complete factual image of the company\'s contribution to society. Additionally, interactive dashboards allow scalable solutions across industries and regions to enable comparative analysis and trends. By including real-time analysis and external voices, this study demonstrates the development of CSR assessment and the potential of AI-based solutions to enhance corporate accountability, transparency, and positive social impact. Further work is being done to improve the integration of low-context models that affect evaluation models, large data sources, multilingual analysis, and CSR models that affect models within the domain of CSR.

References

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Copyright

Copyright © 2025 Bhoomika S Kolekar, Dwithi K, Sathvika J S, Varsha B U, Ganesh H J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.