Comparative Analysis of Training Throughput and Inference Latency in PyCaret Ensembles versus BiLSTM with Attention for

Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper presents a comprehensive benchmarking study comparing a Machine Learning (ML) approach via the PyCaret AutoML framework against a Deep Learning (DL) approach based on a Bidirectional Long Short-Term Memory (BiLSTM) architecture with an Attention mechanism for binary sentiment classification on Indonesian product reviews. The dataset comprises 19,728 samples balanced e

Research goal: How do training throughput and inference latency compare between PyCaret-optimized ensemble models and BiLSTM with attention for binary sentiment classification in resource-constrained environments?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.9/10.