Inspiration
Online review platforms are plagued by spam, irrelevant content, and unverified complaints, making it hard for users to trust location reviews. We wanted to build a system that automatically filters out the noise, ensuring only genuine, relevant, and helpful reviews are surfaced.
What it does
[DayOne] Filtering The Noise is a machine learning pipeline that analyzes Google location reviews to:
- Detect advertisements, spam, and irrelevant content
- Assess review relevance to the business/location
- Identify emotional rants and unconstructive feedback
- Score reviews for quality and usefulness
- Flag or filter reviews that violate platform policies
How we built it
We built a batch-processing pipeline using transformer-based models for spam detection, sentiment analysis, semantic similarity, and image classification. The system processes large datasets, tags reviews with multiple quality metrics, and exports comprehensive results to CSV. We also developed a FastAPI backend for real-time review analysis.
Challenges we ran into
- Integrating multiple transformer models efficiently
- Handling large-scale data processing and memory management
- Designing robust relevance and quality scoring mechanisms
- Ensuring the pipeline is extensible and easy to use
- Validating performance
Accomplishments that we're proud of
- End-to-end ML pipeline with multi-layer analysis
- Real-time API for review evaluation
- Comprehensive tagging and scoring of reviews
- Successfully filtered out spam and irrelevant content from real-world datasets
What we learned
- Practical challenges of batch ML processing
- Importance of multi-dimensional review analysis
- How transformer models can be combined for robust content moderation
What's next for [DayOne] Filtering The Noise
- Expand to more regions and languages
- Integrate user feedback for continuous improvement
- Add more sophisticated image and text analysis layers
- Deploy as a scalable cloud service for review platforms
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