Indonesian marketplace reviews mix standard vocabulary with slang, regional loanwords, numeric shorthands, and emoji, making lexicon-based sentiment tools unreliable in practice. This paper describes a two-track classification pipeline applied to the PRDECT-ID dataset, which contains 5,400 product reviews from 29 Indonesian e-commerce categories, each labeled for binary sentiment (Positive/Negative) and five-class emotion (Happy, Sad, Fear, Love, Anger). The first track applies TF-IDF vectorization with a PyCaret AutoML sweep across standard classifiers. The second track is a PyTorch Bidirectional Long Short-Term Memory (BiLSTM) network with a shared encoder and two task-specific output heads. A preprocessing module applies 14 sequential cleaning steps, including a 140-entry slang dictionary assembled from marketplace corpora. Four configurations are benchmarked: BiLSTM Baseline, BiLSTM Improved, BiLSTM Large, and TextCNN. Training uses class-weighted cross-entropy loss, ReduceLROnPlateau scheduling, and early stopping. Both tracks are deployed as Gradio applications on Hugging Face Spaces. Source code is publicly available at https://github.com/ikii-sd/pba2026-crazyrichteam.

翻译：印尼电商评论混合了标准词汇、俚语、区域外来词、数字简写及表情符号，导致基于词典的情感工具在实际应用中不可靠。本文针对PRDECT-ID数据集提出了一种双轨分类流程，该数据集包含来自29个印尼电商类别的5400条产品评论，每条评论标注了二元情感（正面/负面）和五类情绪（快乐、悲伤、恐惧、喜爱、愤怒）。第一轨采用TF-IDF向量化结合PyCaret AutoML对标准分类器进行扫描。第二轨采用基于PyTorch的双向长短期记忆网络（BiLSTM），其结构包含共享编码器和两个任务特定输出头。预处理模块执行包含14步清洗步骤的流程，其中包含从电商语料库汇编的140条俚语词典。对四个配置进行基准测试：BiLSTM基线模型、BiLSTM改进模型、BiLSTM大型模型和TextCNN。训练采用类别加权交叉熵损失、ReduceLROnPlateau学习率调度和早停法策略。两个轨道的模型均部署为Hugging Face Spaces上的Gradio应用程序。源代码公开发布于https://github.com/ikii-sd/pba2026-crazyrichteam。