Content Analysis Skill

Analyze text content using advanced NLP techniques and LLM-powered insights to extract sentiment, topics, and actionable intelligence from various content sources.

Quick Start

This skill helps you:

Analyze sentiment using both traditional NLP and LLM methods Extract topics and keywords from large text datasets Classify and cluster content automatically Identify viral content patterns and characteristics Generate content insights and recommendations Support multiple languages and content formats When to Use Social Media Analysis: Facebook, Twitter, Instagram, Weibo posts Content Marketing: Blog posts, articles, marketing copy analysis Video Content: YouTube titles, descriptions, comments analysis Product Reviews: Amazon, e-commerce customer feedback News Analysis: Article categorization, sentiment tracking Customer Feedback: Support tickets, surveys, reviews analysis Key Requirements Traditional NLP Analysis pip install pandas numpy matplotlib seaborn nltk scikit-learn wordcloud

LLM-Enhanced Analysis (Optional) pip install openai dashscope # For OpenAI and Qwen API access

Setup NLTK Data import nltk nltk.download('vader_lexicon') nltk.download('punkt') nltk.download('stopwords')

Core Workflow 1. Data Preparation

Your data should include:

Text Content: Main text to analyze (titles, descriptions, comments, etc.) Metadata: Optional (author, date, category, engagement metrics) Multiple Languages: Support for English, Chinese, and other languages 2. Analysis Process Text Preprocessing: Clean, tokenize, and normalize text Sentiment Analysis: Traditional VADER + LLM-enhanced analysis Topic Extraction: TF-IDF keywords + LLM semantic topics Content Classification: Automated categorization and clustering Pattern Recognition: Identify viral content characteristics Insight Generation: Actionable recommendations 3. Output Deliverables Sentiment analysis reports with confidence scores Topic models and keyword extractions Content classification results Viral content pattern analysis Optimization recommendations Example Usage Scenarios Social Media Content Analysis

Analyze Twitter posts for brand sentiment

Identify trending topics and hashtags

Measure engagement patterns

YouTube Video Analysis

Analyze video titles and descriptions

Extract topics from comments

Identify viral content patterns

Product Review Analysis

Analyze customer feedback sentiment

Extract product feature mentions

Identify improvement opportunities

Key Analysis Methods Traditional NLP Techniques VADER Sentiment Analysis: Rule-based sentiment scoring TF-IDF Keyword Extraction: Statistical term importance Text Clustering: K-means and hierarchical clustering Word Frequency Analysis: Term frequency and co-occurrence Language Detection: Automatic language identification LLM-Enhanced Analysis Context-Aware Sentiment: Nuanced emotion understanding Semantic Topic Extraction: Meaning-based topic identification Content Summarization: Automatic text summarization Multi-Language Support: Cross-lingual analysis Zero-Shot Classification: Categorization without training data Advanced Analytics Time Series Analysis: Content trends over time Engagement Prediction: Predict viral potential Competitive Analysis: Compare content performance Audience Insights: Demographic and preference analysis Common Business Questions Answered What is the overall sentiment toward our brand? Which topics are trending in our industry? What makes content go viral? How does sentiment vary by demographic or region? What are customers saying about our products? Which content formats perform best? Integration Examples

See examples/ directory for:

basic_content_analysis.py - Traditional NLP analysis llm_enhanced_analysis.py - LLM-powered analysis social_media_analysis.py - Social media specific analysis Sample datasets for testing LLM Configuration Supported LLM Providers OpenAI: GPT-3.5, GPT-4 models Qwen (通义千问): Chinese-optimized models Open Source: Local models via HuggingFace API Setup Examples

OpenAI Configuration

import openai openai.api_key = 'your-api-key'

Qwen Configuration

import dashscope dashscope.api_key = 'your-api-key'

Best Practices Data Quality: Ensure clean, consistent text data Sampling Strategy: Use representative samples for LLM analysis Cost Management: Balance traditional NLP with LLM calls Language Handling: Configure appropriate language models Validation: Cross-validate sentiment analysis results Privacy: Ensure compliance with data protection regulations Performance Optimization For Large Datasets Use data sampling for LLM analysis Implement batch processing Cache LLM responses when possible Use traditional NLP for initial filtering Cost Management Prioritize important content for LLM analysis Use traditional NLP for bulk processing Implement smart sampling strategies Monitor API usage and costs Advanced Features Real-time Analysis: Stream processing for live content Multi-modal Analysis: Text + image + video content Custom Models: Fine-tune models for specific domains Integration APIs: Connect with content management systems Automated Reporting: Scheduled analysis and reporting Troubleshooting Common Issues Low Sentiment Accuracy: Check language settings and text preprocessing High API Costs: Optimize sampling and caching strategies Slow Processing: Implement parallel processing and batching Language Support: Ensure appropriate models for non-English content Performance Tips Pre-process text data effectively Use appropriate model sizes for tasks Implement result caching Monitor resource usage and optimize