content-analysis

Sentiment and topic extraction analyze text using NLP to identify emotional polarity, themes, and keywords. This Skill combines traditional NLP with optional LLM-powered analysis to process social media, reviews, and marketing copy across multiple languages, returning structured sentiment scores, topic models, and keyword lists for batch or real-time scenarios.

Yes. This Skill performs multilingual sentiment detection and text analysis across social media, reviews, product feedback, and video metadata. It uses NLTK and scikit-learn for traditional NLP alongside optional LLM integration, supporting language-aware processing to extract emotions, topics, and actionable insights regardless of source language.

Pattern detection for virality combines sentiment scoring, topic modeling, and clustering to identify audience preferences and content trends. This Skill classifies and clusters content automatically, then outputs trend indicators and audience insights using pandas, wordcloud, and visualization tools to surface which themes and emotional signals drive engagement.

Both modes work. This Skill supports traditional NLP via NLTK and scikit-learn for immediate results, or hybrid analysis that combines traditional NLP with optional LLM enhancement for richer interpretation. Choose based on your accuracy needs and latency constraints—traditional NLP alone handles high-volume batch processing without external dependencies.

Visualization outputs are generated using Plotly, Matplotlib, and Wordcloud to display sentiment distributions, topic models, and keyword frequencies. After running sentiment and topic extraction on your text data, the Skill produces structured results including classifications and trend indicators, which render as interactive charts and word clouds for pattern discovery.

Prepare text data with optional metadata such as language and source. Pandas handles data ingestion and alignment; provide text entries in a structured format. The Skill then applies NLTK and scikit-learn preprocessing—tokenization, normalization, and vectorization—before running sentiment detection, topic modeling, and keyword extraction to return actionable insights.