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 3mo 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.
How to Use
["Define your scope: Specify the social platform (e.g., Twitter, Instagram), brand/topic, and time period for analysis.","Select tools: Choose sentiment analysis tools (e.g., VADER, TextBlob) and topic modeling tools (e.g., BERTopic, LDA) based on your data size and technical comfort.","Gather data: Use platform APIs or tools like Hootsuite, Brandwatch, or Sprout Social to collect relevant posts, comments, and metadata.","Run analysis: Apply the prompt template to generate insights, then refine by adjusting parameters (e.g., time period, sentiment thresholds) as needed.","Validate and act: Cross-check AI-generated insights with manual sampling to ensure accuracy, then prioritize actionable findings for your marketing team."]
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
Conduct a comprehensive social network analysis of [PLATFORM] for [BRAND/TOPIC] over the past [TIME PERIOD]. Focus on: 1) Identifying the top 5 most influential users and their sentiment trends toward [BRAND/TOPIC], 2) Detecting emerging topics or hashtags using topic modeling, 3) Mapping the network structure to find tightly-knit communities, and 4) Classifying overall sentiment as positive/neutral/negative. Use [SENTIMENT_ANALYSIS_TOOL] for sentiment scoring and [TOPIC_MODELING_TOOL] for topic extraction. Prioritize insights that could inform [MARKETING_CAMPAIGN/STRATEGY].
Example Output
Analysis of Twitter discussions about the fictional beverage brand 'ZestAde' over Q2 2024 reveals critical insights for their summer campaign. The network consists of 12,478 unique users generating 45,231 tweets. Sentiment analysis using VADER shows 62% positive sentiment (up from 58% in Q1), 23% neutral, and 15% negative. The top influential user is @FitnessGuru (120k followers) who posted 3 positive reviews about ZestAde’s hydration benefits, driving a 8% spike in engagement for their tweet. Topic modeling with BERTopic identified 3 dominant themes: 'summer hydration' (42% of discussions), 'flavor innovation' (28%), and 'sustainability concerns' (15%). Network analysis reveals 4 major communities: fitness enthusiasts (38% of users), health-conscious parents (22%), eco-conscious millennials (18%), and price-sensitive students (12%). The 'sustainability concerns' topic is concentrated in the eco-conscious community, where 68% of mentions are negative, specifically targeting ZestAde’s plastic packaging. Recommendations include: 1) Partnering with @FitnessGuru for a sponsored hydration challenge, 2) Launching a limited-edition aluminum-can version targeted at eco-conscious users, and 3) Creating content addressing sustainability concerns in the health-conscious parent community.
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