针对印尼电商评论的复杂语言特点，提出多任务双向LSTM模型提升情感与情绪分类效果。
Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking
- 采用双轨架构：传统机器学习与深度学习并行对比
- 在5400条评论上实现93.2%情感准确率与81.7%情绪分类准确率
- 适合研究东南亚多语种文本分析或电商智能客服的开发者
印尼电商平台评论混合使用标准词汇、俚语、地方借词、数字缩写和表情符号，使基于词典的情感工具难以可靠应用。本文针对包含5400条来自29个印尼电商品类的评论数据集PRDECT-ID，构建双轨分类流程：第一轨采用TF-IDF向量化与PyCaret AutoML对多种标准分类器进行自动筛选；第二轨为基于PyTorch的双向长短期记忆网络（BiLSTM），共享编码器并设两个任务专用输出头。预处理模块执行14步清洗流程，包括基于市场语料构建的140项俚语词典。共比较四种配置：BiLSTM Baseline、BiLSTM Improved、BiLSTM Large及TextCNN。训练使用类别加权交叉熵损失、ReduceLROnPlateau学习率调度和早停策略。两套系统均部署于Hugging Face Spaces的Gradio应用中，代码已公开于https://github.com/ikii-sd/pba2026-crazyrichteam。
原文摘要 · Abstract (English)
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.
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