Summary
This arXiv paper (2504.20612) by Hermawan Manurung, Ibrahim Al-Kahfi, and Ahmad Rizqi addresses sentiment and emotion classification for Indonesian e-commerce reviews, where mixed slang, regional loanwords, numeric shorthands, and emoji render lexicon-based tools unreliable. The authors evaluate a two-track pipeline on the PRDECT-ID dataset, which contains 5,400 product reviews across 29 Indonesian e-commerce categories, labeled for binary sentiment (Positive/Negative) and five-class emotion (Happy, Sad, Fear, Love, Anger). The first track combines TF-IDF vectorization with a PyCaret AutoML sweep across standard classifiers; the second is a PyTorch Bidirectional LSTM (BiLSTM) network with a shared encoder and two task-specific output heads for joint sentiment and emotion prediction. Preprocessing applies 14 sequential cleaning steps, including a 140-entry slang dictionary compiled from marketplace corpora.
Paper Overview
- Field: NLP
- Authors: Hermawan Manurung, Ibrahim Al-Kahfi, Ahmad Rizqi
- Published: 2025-04-29
- arXiv: 2504.20612
Abstract
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 compiled from marketplace corpora.
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