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Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Microbloggers.

Jerry Chongyi Hu¹, Mohammed Shahid Modi¹, Boleslaw K Szymanski¹

¹Department of Computer Science and Network Science and Technology Center, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.

Entropy (Basel, Switzerland)

|March 28, 2025

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Analyzing public sentiment on China's Weibo during the COVID-19 crisis reveals how opinions shift. This study used Llama 3 8B to track sentiment changes, offering insights into societal responses during health emergencies.

Area of Science:

Social Sciences
Computational Linguistics
Public Health

Background:

Understanding public sentiment during crises is vital for analyzing societal polarization.
The COVID-19 pandemic highlighted the need for real-time analysis of public opinion.
Chinese social media platforms like Weibo are crucial for gauging public discourse.

Purpose of the Study:

To analyze public sentiment shifts on Weibo during the COVID-19 crisis.
To investigate the influence of social events and government actions on public opinion.
To contribute to sentiment analysis research on Chinese digital platforms during health crises.

Main Methods:

Utilized Weibo posts from pre-COVID-19, outbreak, and early prevention stages.
Employed Llama 3 8B, a large language model, for sentiment classification.

Keywords:

COVID-19 Weibo sentiment analysis social media

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Categorized user sentiments into positive, negative, sarcastic, and neutral.

Main Results:

Identified significant shifts in public sentiment throughout the COVID-19 crisis stages.
Demonstrated the impact of epidemic-related events on user opinions expressed on Weibo.
Provided a nuanced understanding of sentiment dynamics beyond simple positive/negative metrics.

Conclusions:

Sentiment analysis on Weibo offers critical insights into public reactions during health crises.
Digital communication plays a significant role in shaping societal responses to global challenges.
This study addresses a gap in sentiment analysis for Chinese social media during public health emergencies.