Reddit Sentiment Analysis

Repository Contents

• Dataset: Reddit_Data.csv
• Training and Evaluation Scripts
• Model Prediction Tools: manual_test.py
• SHAP Explanation Tools
• Scripts for Generating Plots and Analysis:
  • plot_hyperparameter_effects.py
  • rank_best_models.py

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Reproducing Results

1. Clone the Repository

git clone https://github.com/aonlo/redditsentimentanalysis
cd redditsentimentanalysis

2. Install Dependencies

Use a virtual environment and install the required packages (e.g., TensorFlow, pandas, matplotlib, scikit-learn, seaborn, SHAP):

pip install -r requirements.txt

3. Train and Evaluate All Models

Run the main training script to conduct the full grid search:

python sentiment_training.py

This will:

• Train all 54 model configurations
• Save each model
• Record evaluation metrics to grid_search_results.csv

⚠️ Note: This process may take several hours depending on your device’s computing power. An estimate for the remaining time is displayed in the console and improves as more models are trained.

4. Generate Visualizations

Once training is complete:

python plot_hyperparameter_effects.py
python rank_best_models.py

5. Review the Results

• Top 10 models will be saved in top_10_models.csv
• Metric comparison plots will appear in plots/ and top_model_plots/
• Use manual_test.py to load any trained model and test predictions on manual input or a new CSV file.

6. (Optional) Interpret Predictions

If SHAP is enabled in manual_test.py, the script will generate SHAP visualizations for word-level attribution in sentiment predictions.