Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers

TL;DR

This paper leverages Llama 3 8B, a large language model, to analyze non-binary COVID-19 sentiments on Chinese Weibo posts, revealing how social and governmental factors influence public opinion during health crises.

Contribution

It introduces a novel application of Llama 3 8B for classifying nuanced sentiments on Chinese social media during COVID-19, addressing a gap in sentiment analysis for non-English platforms.

Findings

Sentiment shifts correlate with key social events and government actions.

Llama 3 8B effectively classifies complex sentiments including sarcasm.

Insights into public opinion dynamics during health crises.

Abstract

Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication's role in shaping society's responses during unprecedented global challenges.