Comparison of LSTM and TCN Models for Customer Churn Prediction Based on Sentiment and Transaction Data

This study investigates the combined use of customer review sentiment analysis and transaction history to predict customer churn on the Balimall Market e-commerce platform. The dataset includes 41,519 reviews labeled with positive and negative sentiments and 48 transaction samples labeled as churn or non-churn based on RFM method. Two deep learning models, Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), are applied in parallel for each analysis path. Data pre-processing i