A Bidirectional LSTM that classifies Indonesian public comments about a government policy (the Kopdes program) into three sentiment classes: Negatif, Netral, and Positif. The model is trained from scratch (no pretrained embeddings) on a small dataset, so treat it as a course/research-level baseline rather than a production classifier. - About 850 unique comments remained after preprocessing and deduplication. - Labels are not human-annotated. They were generated automatically with the InSet lexicon: score = (number of positive words) − (number of negative words) in the stemmed text. Score > 0 is Positif, < 0 is Negatif, and 0 is Netral. Because of this, the reported scores measure how well…
Independent publisher
Dante
whotao1766
AI, Cyber, Network
Models in Library1
Datasets in Library0
Models on Hugging Face3
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Published tasks: Text classification.
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