RESEARCH-ARTICLE
A Comparative Study of China's National Image Before and After the COVID-19 Pandemic Based on Twitter Text Mining
This study analyzes the shifts in China's national image pre and post-COVID-19 pandemic using Twitter text mining. It categorizes sentiments into sovereignty, territory, population, enterprise, and government, revealing an initial negative international sentiment towards China's population, government, and enterprises. However, proactive measures from the Chinese government led to a gradual recovery, stabilizing near pre-pandemic levels. The research provides empirical insights for enhancing China's global image.
Executive Impact & Key Findings
Our analysis of 3.9 million English tweets reveals a complex evolution of China's national image. While initially impacted by the COVID-19 pandemic, leading to sharp declines in sentiment across government, enterprises, and population, proactive governmental responses and a focus on recovery led to a significant rebound. Sentiment for sovereignty and territory was more influenced by geopolitical events than the pandemic itself. Overall, China's national image on Twitter demonstrated considerable resilience, recovering to or surpassing pre-pandemic levels by late 2022.
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Tweets Analyzed
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Samples Labeled
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SVC Model Precision
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Govt. Sentiment Drop (Early COVID)
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Corp. Sentiment Drop (Early COVID)
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Pop. Sentiment Drop (Early COVID)
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Sovereignty Sentiment Spike (Mar 2021)
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Territory Sentiment Drop (Aug 2022)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Territory Image
Sovereignty Image
Population Image
Corporate Image
Government Image
3400%
Sharp sentiment decline in August 2022 due to Pelosi's Taiwan visit, a geopolitical event.
Data Analysis Workflow for Territory Image
Twitter API Data Collection
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English Tweet Pre-processing
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Text Classification (SVC Model)
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Sentiment Analysis (TextBlob)
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Sentiment Index Visualization
1840%
Sentiment spike in March 2021 following the Anchorage meeting between US and Chinese officials.
| Phase | Key Sentiment Characteristics |
|---|---|
| Pre-Pandemic |
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| Early Pandemic (Jan-Mar 2020) |
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| Later Pandemic (Mid-late 2020 - Dec 2022) |
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Resilience of Chinese Enterprises Post-Pandemic
Initially, Chinese enterprises faced significant challenges, with sentiment dropping 253% in early Stage 2 (Feb-Mar 2020) due to global pandemic outbreaks and border-closing policies. Keywords like "LOCKDOWN", "PANDEMIC", "SHIPMENT", and "BLOCK" dominated discussions. However, as conditions improved, sentiment gradually recovered, stabilizing with an upward trend through Stage 3. By December 2022, the Chinese corporate image surpassed pre-pandemic levels, demonstrating remarkable resilience and adaptability in the global market.
Key Metric: Sentiment Recovery: Beyond Pre-Pandemic
1205%
Sharp sentiment decline in early 2020 due to distorted information and anti-China forces regarding COVID-19 control measures.
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Your AI Implementation Roadmap
A phased approach to integrate advanced AI text mining and sentiment analysis into your enterprise for strategic intelligence.
Phase 1: Discovery & Strategy Alignment
Initial consultations to understand your specific business objectives, data sources, and desired outcomes. Define key performance indicators (KPIs) and tailor the AI solution to your strategic goals.
Phase 2: Data Engineering & Model Customization
Securely integrate your data sources (e.g., social media APIs, internal communications). Customize and train AI models (like SVC and TextBlob) for your specific text data, ensuring high accuracy and relevance.
Phase 3: System Deployment & Integration
Deploy the AI sentiment analysis platform within your existing enterprise infrastructure. Ensure seamless integration with current reporting tools and user dashboards for easy access to insights.
Phase 4: Training & Optimization
Provide comprehensive training for your teams on how to leverage the AI insights. Continuously monitor model performance, gather feedback, and iterate for ongoing optimization and enhanced accuracy.
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