Reddit Post Popularity Prediction

The dataset contains 500,000 reddit news posts including information of post itme, author, over-18, and titles. The data is tabular and the features involved should be self-explanatory.

Prediction task: predict log upvotes of reddit post

• Target: log upvotes of reddit
• Evaluation metrics: RMSE, MAE, Spearman Rho

Instruction

• model folder contains LSTM and CNN model implemented using pytorch
• Dataloader, NNRegressor is used for training deep learning model

# Run LSTM-FUSI model
python3 main.py

• Report.ipynb include all results and analyzing process
• result.pdf shows the result for all models

Model Summary

I tried 5 models

• Catboost + TFIDF + SVD
• Catboost + Word2vec/Fasttet
• Catboost + Pretrained Bert
• Catboost + [TFIDF + SVD, Word2vec, Bert]
• End-to-end Model - LSTM encoder + time/author/other features embeddings + Fully Connected NN

Result shows LSTM + feature fusing has the best result