Industry: Media & Entertainment/Data Analytics
Developed for media research firms and entertainment news aggregators to automate cross-platform trend analysis and content curation.
Problem
Media professionals struggle to track fragmented narratives across competing publications, leading to redundant manual labor and delayed reporting.
- Information Overload: Analysts spend ~15 hours weekly manually monitoring 8+ industry-leading sources for overlapping stories.
- High Latency in Discovery: Significant delays in identifying "hot" topics that are gaining traction across multiple outlets simultaneously.
- Manual Synthesis: High costs associated with hiring researchers to summarize and categorize weekly industry shifts.
Solution
An automated parsing and NLP-driven analysis system that aggregates, de-duplicates, and summarizes TV/film industry news.
- Automated Cross-Referencing: Intelligent detection of overlapping content (e.g., Yellowstone production updates) across 4 leading websites using Scikit-learn clustering.
- Intelligent Summarization: High-fidelity news digests generated via OpenAI’s GPT-4, reducing 2,000+ words of source text into 200-word actionable briefs.
- Real-time Data Structuring: Conversion of unstructured web data into a searchable MySQL database for historical trend mapping.
- Optimized Performance: Built on FastAPI to handle concurrent parsing tasks without bottlenecking the user interface.
Tech Stack
- Analysis: NLTK & Scikit-learn for NLP and content similarity clustering.
- Backend: FastAPI for high-concurrency API management and parsing logic.
- Database: MySQL for structured storage of historical media data and entity relations.
- AI/LLM: OpenAI API for automated thematic synthesis and digest generation.
- Data Handling: Pandas for high-speed transformation of parsed web data.
Results
- 80% Reduction in Research Time: Automated the monitoring of 8 core industry pages, saving analysts an average of 12 hours per week.
- 40% Faster Trend Identification: Real-time overlap detection allowed the client to identify trending TV shows 4-6 hours ahead of manual competitors.
- Significant Operational Savings: Reduced the need for junior content curators, cutting editorial overhead costs by roughly $4,500 per month.
- 100% Data Accuracy: Eliminated human error in cross-referencing article sources and release dates across fragmented platforms.