Enterprise AI Analysis
Public attitudes toward DeepSeek on Chinese social media: a study based on sentiment analysis and topic modeling
DeepSeek has emerged as a prominent representative of China's domestic large language models (LLMs), attracting widespread public attention and generating diverse discussions on social media since its release. This study systematically examines public sentiment and thematic concerns surrounding DeepSeek by analyzing Weibo posts.
Executive Impact Summary
Key findings at a glance, highlighting the core implications for enterprise strategy and innovation.
- Analyzed 86,008 Weibo posts, retaining 59,679 valid entries after cleaning.
- Utilized a mixed-method approach: LDA for topic modeling and fine-tuned BERT for sentiment analysis.
- BERT model achieved 82% accuracy on 10,000 hand-labeled posts.
- Identified nine major themes in public discourse.
- Found 46.3% positive, 39.6% neutral, and 14.0% negative sentiment.
- Observed sentiment peaks on Jan 27 and March 19, 2025.
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Posts Analyzed
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BERT Accuracy
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Positive Sentiment
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Key Topics Discovered
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overall Findings
Methodology
Societal Context
Public Sentiment Overview
Public attitudes toward DeepSeek are generally favorable, with positive sentiment significantly outweighing negative discussions.
46.3%
of posts expressed positive attitudes
2309
posts due to official release & App Store surge (Jan 27)
1989
posts due to Weibo Zhishou integration (Mar 19)
Research Framework & Model Validation
A robust mixed-method approach combining topic modeling and deep learning sentiment analysis was employed.
Analytical Methodological Framework
Data Collection
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Data Preprocessing
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Statistic Analysis
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Time Trend Analysis
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Topic Modeling
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Discovered Topics
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Sentiment Analysis
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Sentiment Proportions
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Sentiment Trends
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Topic Distribution
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Conclusion & Discussion
| Metric | LDA | BERTopic |
|---|---|---|
| Coherence (c_v) | Higher | Lower |
| Stability (Jaccard) | More Consistent | Less Consistent |
| Computational Cost | Lower (full corpus) | Higher (full corpus) |
DeepSeek's Distinctive Role
DeepSeek's reception highlights unique socio-political and industrial dimensions compared to ChatGPT's application-focused framing.
| Aspect | DeepSeek (Weibo) | ChatGPT (Weibo/Twitter) |
|---|---|---|
| Overall Sentiment | Predominantly positive (46.3%), with structured neutral (39.6%) and critical (14.0%) voices | Generally positive, with early discussions also containing neutral and cautious views |
| Key Themes | Industrial transformation, technological self-reliance, China-US tech rivalry | Education, writing assistance, productivity, ethics, privacy risks |
| Framing Orientation | National technological sovereignty, industrial embedding, and geopolitical competition | Application-oriented value, ethical responsibility, and productivity gains |
| Representative Concerns | Data security, stability, hardware dependency, geopolitical risks | Plagiarism, misinformation, privacy risks, academic integrity |
Estimate Your Enterprise AI ROI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your Enterprise AI Roadmap
A structured approach to integrating DeepSeek-like LLMs into your organization, from discovery to optimization.
Phase 1: Discovery & Strategy Alignment (2-4 Weeks)
Identify key use cases, assess data readiness, and define success metrics. Includes workshops with stakeholders and a detailed strategy report.
Phase 2: Pilot Program Development (4-8 Weeks)
Develop and deploy a small-scale pilot for a critical use case. Focus on integration, fine-tuning, and initial user feedback collection.
Phase 3: Scaled Deployment & Optimization (Ongoing)
Expand AI solutions across relevant departments. Implement continuous monitoring, performance optimization, and user training.
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