PyTorch Forum Topic Analysis

Comprehensive analysis of 73,000+ PyTorch Forum topics using semantic embeddings, unsupervised clustering, and interactive visualization

Project Overview

This project demonstrates advanced data science techniques applied to real-world forum data, showcasing the power of semantic embeddings and unsupervised learning for understanding large-scale text collections.

Dataset

  • Source: PyTorch Community Forum
  • Format: JSONL with topic metadata
  • Volume: 73k+ topics across multiple categories
  • Fields: ID, title, category, views, replies, dates

Pipeline Architecture

flowchart TD A[JSONL Data
73k topics] --> B[DataLoader
Clean & Validate] B --> C[EmbeddingGenerator
all-MiniLM-L12-v2
384-dim vectors] C --> D[ClusteringEngine
K-Means + HDBSCAN] C --> E[VisualizationGenerator
UMAP/t-SNE + Plotly] D --> F[EvaluationEngine
Metrics & Validation] D --> G[ClusterAnalyzer
TF-IDF + Word Clouds] D --> H[AdvancedAnalyzer
Temporal Trends] F --> I[Interactive Report] G --> I H --> I E --> I style A fill:#e1f5fe style I fill:#c8e6c9

Technical Implementation

Processing Workflow

graph LR A[Load JSONL] --> B[Clean Data] B --> C[Generate Embeddings
384-dim vectors] C --> D[Find Optimal Clusters
K-Means silhouette] D --> E[Create Visualizations
2D/3D plots] D --> F[Analyze Clusters
Keywords & patterns] E --> G[Generate Report] F --> G

Key Technologies

  • Embeddings: sentence-transformers/all-MiniLM-L12-v2 (384-dim)
  • Clustering: K-Means with auto-K selection, HDBSCAN optional
  • Visualization: UMAP/t-SNE + Plotly interactive plots
  • Analysis: TF-IDF keywords, temporal trends, word clouds

End-to-End Workflow

This project processes 73k PyTorch Forum topic titles through a complete pipeline:

  1. Load & clean JSONL data (DataLoader)
  2. Embed titles with Sentence-Transformers
  3. Cluster embeddings via K-Means (optimal k) or HDBSCAN
  4. Visualize clusters in 2D/3D (UMAP / t-SNE)
  5. Evaluate with silhouette, NMI, ARI metrics
  6. Analyze clusters: keywords, word-clouds, trends, correlations
  7. Report – you're reading it!

Data Files & Outputs

Directory Structure

graph TD A[experiments/] --> B[embeddings/] A --> C[clusters/] A --> D[visualizations/] A --> E[evaluation/] A --> F[analysis/] B --> B1[topic_embeddings.npy
topic_embeddings.json] C --> C1[kmeans_labels.npy
kmeans_meta.json] D --> D1[2d_clusters.html
3d_clusters.html] E --> E1[metrics.json
confusion_matrix.png] F --> F1[cluster_characteristics.json
word_clouds/
advanced/] style A fill:#e3f2fd style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 style E fill:#fce4ec style F fill:#f1f8e9

Key Output Files

  • Embeddings: 384-dimensional vectors in NumPy format
  • Clusters: K-Means labels and metadata
  • Visualizations: Interactive 2D/3D Plotly charts
  • Analysis: TF-IDF keywords, word clouds, temporal trends

Interactive Visualizations

Explore clusters through interactive 2D and 3D plots with hover details and zoom capabilities.

Cluster Visualizations

Interactive scatter plots showing topic clusters using dimensionality reduction

Visualization Features

  • Interactive Hover: View topic titles on mouseover
  • Zoom & Pan: Explore dense regions and cluster boundaries
  • Color Coding: Each cluster has distinct colors for easy identification
  • Responsive Design: Adapts to different screen sizes

Full Screen Links: 2D Plot | 3D Plot

Clustering Evaluation

Metrics Overview

graph LR A[Clustering Quality] --> B[Unsupervised
Silhouette Score] A --> C[Supervised
vs Forum Categories] B --> B1[Internal Cohesion
Cluster Tightness] C --> C1[ARI: Agreement
NMI: Information
V-Measure: Balance]

Key Metrics

Metric Range Best Measures
Silhouette Score[-1, 1]→ 1Cluster separation
Adjusted Rand Index[-1, 1]→ 1Agreement with truth
Normalized Mutual Info[0, 1]→ 1Information sharing
V-Measure[0, 1]→ 1Balanced quality

Results

Confusion Matrix: Clusters vs Categories
Confusion matrix showing cluster vs category alignment

Cluster Analysis

Analysis Pipeline

graph LR A[Clustered Topics] --> B[TF-IDF Analysis] A --> C[Representative Titles] A --> D[Word Cloud Generation] B --> E[Top Keywords
per Cluster] C --> F[Cluster
Characteristics] D --> G[Visual
Summaries] E --> H[JSON Report] F --> H G --> H

Cluster Categories Discovered

  • Technical: PyTorch modules, functions, APIs
  • Domain-Specific: Computer Vision, NLP, Reinforcement Learning
  • Problem-Solution: Debugging, troubleshooting, error resolution
  • Educational: Tutorials, learning resources, best practices
  • Research: Advanced techniques, academic papers, cutting-edge methods

Data & Visualizations

Word Clouds by Cluster

Hover over each word cloud to see cluster keywords. Size indicates term importance within the cluster.

Advanced Analytics

Temporal & Engagement Analysis

graph TD A[Forum Data] --> B[Temporal Trends
Posts over time] A --> C[Engagement Patterns
Views & replies] A --> D[Content Categories
Evergreen vs trending] B --> E[Quarterly Analysis
Topic evolution] C --> F[Controversy Detection
High-reply topics] D --> G[Value Assessment
Long-term relevance]

Generated Insights

Forum Activity Timeline
Posts per month showing forum activity trends
Topic Trends Heatmap
Topic trends heatmap showing cluster evolution
Engagement Distribution
Reply distribution showing engagement patterns

Detailed Results

  • Evergreen Posts 
    Loading evergreen posts analysis...
  • High-Engagement Posts 
    Loading high-engagement posts analysis...
  • Correlation Analysis 
    Loading correlation analysis...

Data Science Pipeline: From raw forum data to actionable insights through state-of-the-art NLP and clustering techniques

All visualizations are interactive • Data is anonymized • Code is modular and extensible