NLP and Network Analysis of ChatGPT Conversations
This project studied social media conversations about ChatGPT using a combination of exploratory data analysis, graph/network analysis, and natural language processing.
Analytical approach
The work moved through three layers:
- Engagement analysis: replies, reposts, likes, quotes, hashtags, and post-level activity.
- Network analysis: centrality, clustering, communities, and relationship structure.
- Text analytics: sentiment analysis, topic modeling, named-entity recognition, and information extraction.
Why it matters
ChatGPT became a cross-industry topic almost immediately. The conversation included technologists, companies, educators, media accounts, influencers, and everyday users. That made it a useful case for studying how attention forms around a new technology and how public interpretation changes across communities.
Portfolio relevance
The project shows how analytics and NLP can be combined to understand fast-moving public discourse. It also connects to agentic AI research because model behavior is not only technical; it shapes institutions, markets, narratives, and decision-making environments.