Natural Language Processing for Social Good: A Cross-Disciplinary Approach

Abstract

Natural language processing (NLP) has the potential to address societal challenges by analyzing large volumes of text data. This paper explores how NLP techniques, combined with insights from social sciences, public health, and policy analysis, can be used for social good. Applications include sentiment analysis of public opinion, detecting misinformation on social media, and analyzing healthcare records to identify trends. Case studies from Australia illustrate the potential of NLP to inform policy and improve public services. The paper emphasizes the importance of interdisciplinary collaboration to ensure ethical and equitable use of NLP technologies.