Natural Language Processing

Topic Modelling Pada NLP

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Topic Modelling adalah mengelompokan data berdasarkan suatu topik tertentu. Topic modeling bekerja secara unsupervised learning yakni tidak membutuhkan data yang berlabel seperti clustering dengan proses mengelompokan dokumen-dokumen berdasarkan kemiripanya.

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  • Text classification – Topic modeling can improve classification by grouping similar words together in topics rather than using each word as a feature
  • Recommender Systems – Using a similarity measure we can build recommender systems. If our system would recommend articles for readers, it will recommend articles with a topic structure similar to the articles the user has already read.
  • Uncovering Themes in Texts – Useful for detecting trends in online publications for example


  • LDA – Latent Dirichlet Allocation – The one we’ll be focusing in this tutorial. Its foundations are Probabilistic Graphical Models
  • LSA or LSI – Latent Semantic Analysis or Latent Semantic Indexing – Uses Singular Value Decomposition (SVD) on the Document-Term Matrix. Based on Linear Algebra
  • NMF – Non-Negative Matrix Factorization – Based on Linear Algebra
  • PLSA
  • LDA2Vec
  • SVD

Code :

Referensi :

Baca juga :   6 Teknik Dasar Text Preprocessing Pada NLP

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