Public Policy Analysis Using NLP

• Public policy choices significantly shape societal structures (economic, social, environmental).
• Analyzing the effects and public response to policy choices is crucial for transparent and effective governance.
• Traditional methods involve surveys, focus groups, field research, and expert studies.
• Traditional methods have limitations like small sample sizes, long timelines, and high costs.
• Large-scale national surveys can cost over $500,000 and take 3-6 months.

Social Media's Role

• Social media and forums provide a new space for measuring public perception of policy choices.
• As of 2024, there are over 4.95 billion social media users globally.
• Twitter generates nearly 500 million tweets daily, which provides real-time societal insights.

Natural Language Processing (NLP)

• Natural Language Processing (NLP), a branch of AI, effectively extracts meaning from large amounts of unstructured text.
• NLP analyzes user-generated content such as tweets, Facebook updates, Reddit forums, and online comments.
• NLP helps discover patterns, trends, and sentiments in public opinion.
• NLP is useful for tracking the effects of policies by gathering instantaneous feedback.

Applications of NLP

• NLP is effective across policy-related domains, as shown by recent research.
• Green economy policy research in Indonesia used sentiment analysis to gauge support for renewable energy investments.
• Chowdhury et al. used NLP to measure public responses to economic stimulus packages during the COVID-19 pandemic.
• Anoop and Krishnan used topic modeling and sentiment analysis to examine public debate on healthcare reforms.
• ClimateNLP has been used to track sentiments toward environmental policies across demographics.

Capabilities of NLP

• NLP systems offer scalability and flexibility.
• NLP systems can process and analyze over 100,000 documents per minute.
• This enables continuous tracking of public sentiment and opinion shifts.
• Transformer-based models such as RoBERTa show up to 92% accuracy on benchmark sentiment analysis datasets.
• Challenges include interpreting informal language, sarcasm, ambiguity, and ensuring data representativeness across languages and regions.

Challenges and Solutions for NLP

• Hinglish (Hindi and English mix) on social media is a challenge for policy impact analysis in India.
• Hinglish is common on platforms like Twitter, YouTube, and Facebook.
• Conventional NLP models struggle with code-mixed text due to transliteration, grammar, and slang issues.
• This affects sentiment extraction and thematic classification accuracy.
• Strong models suited for multilingual and code-switched data are needed.
• These models should combine contextual embeddings and transfer learning strategies.
• Addressing these issues is crucial for inclusive policy analysis, as many express opinions in blended modes.
• Hinglish increases user involvement but complicates machine comprehension for automated text analysis.

Current Developments in NLP

• NLP-based policy analysis tools are gaining attention from policymakers and academics.
• Deep learning models like BERT, RoBERTa, and GPT refining sentiment analysis techniques.
• NLP-powered predictive governance models help anticipate public reactions to proposed policies.
• NLP can help pre-empt opposition and encourage participatory involvement.
• The intersection of NLP and public policy analysis offers enhanced insights, methods, and future directions.
• Policymakers can leverage language data for citizen-centric governance and strengthened democratic processes.