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Support Vector Machine

3-Stage (AgglomerativeClustering + 2-Stage Hierarchical LinearSVC) News Topic Classifier

Data Science Natural Language Processing Topic Classification Tf-Idf Agglomerative Clustering Support Vector Machine Sentence Transformers Bert Python

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This projects trains a hierarchical text classifier for Indonesian articles using TF-IDF features + LinearSVC, with a data-driven “aggregate topic” stage built via AgglomerativeClustering. Validated on two case studies:

  • an unlabeled Indonesian news corpus (Kaggle 2025) that I labeled efficiently via a cluster-first workflow (SentenceTransformers + BERTopic, then selective manual labeling)
  • a finance-topic dataset consisting of short, headline-like sentences (avg. ~118 characters), to test robustness across different text lengths and class imbalance.

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