Natural Language Processing

Topic Modelling Pada NLP

Pinterest LinkedIn Tumblr

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.

Image result for membuat topic modeling


  • 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 :

Founder & author di, Seorang penikmat coklat panas.

Write A Comment

Pin It