Online-Social-Network-Analysis
🥈SilverExploring the latest algorithms which include sentiment classification, information extraction, clustering, and topic modeling for analyzing online social networks, considering both their structure and content.
400Updated 2mo ago
Intermediate30min to implementmarketing
Saves ~270 min per use
Quick InstallView Source
git clone https://github.com/raj-kotak/Online-Social-Network-Analysis.gitWorks with:
Claude
Overview
About This Skill
Online Social Network Analysis applies advanced machine learning algorithms to extract meaningful insights from social networks. The skill leverages sentiment classification, information extraction, clustering, and topic modeling to analyze both network structure and content simultaneously. It addresses real-world challenges in public health monitoring, crisis response coordination, political analysis, and marketing strategy. Organizations use these techniques to understand network dynamics, identify emerging topics, and gauge public sentiment at scale.
Use Cases
Public health monitoring and disease outbreak detection via social media signals
Crisis response coordination by analyzing network spread and sentiment during emergencies
Political analysis and voter sentiment tracking across social networks
Marketing campaign analysis through sentiment classification and topic discovery
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/raj-kotak/Online-Social-Network-AnalysisRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the latest social media trends for [COMPANY] in the [INDUSTRY] sector. Use sentiment classification, information extraction, clustering, and topic modeling to provide insights. Focus on both the structure and content of the online social networks. Include data from the last [TIMEFRAME] months.
Example Output
# Social Media Trends Analysis for [COMPANY] in [INDUSTRY] ## Sentiment Analysis - **Overall Sentiment**: Positive (62% positive, 28% neutral, 10% negative) - **Key Positive Topics**: Product innovation, customer service, new features - **Key Negative Topics**: Shipping delays, pricing concerns, app crashes ## Topic Modeling 1. **Product Innovation**: Discussions around new features and product launches 2. **Customer Service**: Praise for responsive support and quick issue resolution 3. **Shipping Delays**: Complaints about delayed shipments and logistics issues 4. **Pricing Concerns**: Debates about pricing strategies and value for money ## Clustering - **Cluster 1**: Tech enthusiasts discussing new features and innovations - **Cluster 2**: Customers sharing their positive experiences with customer service - **Cluster 3**: Users frustrated with shipping delays and logistics - **Cluster 4**: Price-sensitive customers discussing value for money ## Information Extraction - **Key Influencers**: @TechGuru, @IndustryInsider, @CustomerAdvocate - **Trending Hashtags**: #Innovation, #CustomerService, #ShippingIssues, #Pricing - **Emerging Trends**: Increased interest in sustainability and eco-friendly products
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