Presentation (CTI)

OBJECTIVES: Inflation has become a critical post-pandemic concern, contributing significantly to societal stress. Social media platforms, like X (formerly Twitter), provide insights into public sentiment and discourse. This study analyzes U.S. pre-election narratives on inflation using sentiment analysis and topic modeling to explore emotional tone and thematic content.
METHODS: A random sample of 9,695 English posts from April 29 to May 5, 2024, was analyzed using a hybrid methodology. Sentiment analysis was conducted with SentiStrength, and topic modeling with machine learning methods was performed using Python 3.10. Generative AI (ChatGPT-4o) analyzed 50 sample posts, validated by human experts from diverse disciplines.
RESULTS: Posts contained significant political content, with 20% mentioning President Biden (1,308 mentions) or ex-President Trump (893 mentions). Frequent terms included "money," "economy," and "price." Sentiment analysis using ChatGPT identified sarcasm alongside positive and negative sentiments. Using a two-step method of topic modeling and hierarchical clustering, the following topics were identified: economic and social frustrations (32.1%), policy and economic affairs (24.5%), economic growth and employment (11.3%), social and security issues (11.3%), energy and cost challenges (10.6%), and opinions and political discourse (10.2%). The analysis revealed that discussions on inflation were more focused on political anger and macroeconomic discourse than personal stress. A comparison of content analysis of 50 posts by ChatGPT and human experts showed 76% unanimous concordance, 12% partial agreement, and 12% disagreement. In cases of partial agreement and disagreement, content analysis using ChatGPT-o1 preview achieved 100% agreement with human experts. However, ChatGPT could not process all 9,695 posts for topic modeling, requiring the use of Python-based tools.
CONCLUSIONS: Sentiment analysis highlighted pervasive negativity about inflation, with discussions extending to socio-political issues. ChatGPT captured emotional tones effectively but faced limitations with large data volumes, suggesting a hybrid approach involving both AI and human expertise is necessary for comprehensive social media analysis.