Reddit Popularity Classification

Overview

This project studies whether Reddit post text can help predict post popularity. It converts post titles and body text into Transformer-based semantic representations, then benchmarks several neural architectures for a three-class classification problem.

The repository is structured as an applied NLP research workflow covering extraction, cleaning, label engineering, embedding generation, imbalance handling, model comparison, and error analysis.

Problem

Popularity prediction is difficult because engagement is noisy, imbalanced, and influenced by language, community context, and timing. A keyword-only model is too shallow for the task, so this project uses contextual Transformer embeddings.

Solution

The workflow turns upvote_ratio into low, average, and high popularity classes, cleans the text fields, combines title and self-text, creates BERT-family embeddings, balances classes, and trains multiple neural classifier heads.

Technical Highlights

• Reddit post extraction and text preparation.
• Label engineering from engagement signals.
• BERT, RoBERTa, ALBERT, and TinyBERT-style embedding comparisons.
• Pooled-output and CLS-token representation testing.
• Oversampling, undersampling, and SMOTE-style class balancing.
• Dense, residual, progressive, CNN, autoencoder, and attention-style model heads.
• Evaluation with accuracy, precision, recall, F1-score, confusion matrices, and visual diagnostics.

Results

Saved reports show several configurations reaching approximately 0.91 to 0.92 accuracy on the evaluated test split of 828 samples. ALBERT and TinyBERT-style configurations were especially competitive in the saved results.

Future Improvements

• Add script-based training in addition to notebooks.
• Track experiments with MLflow or Weights & Biases.
• Add cross-validation and hyperparameter search.
• Compare Transformer embeddings against TF-IDF and classical ML baselines.