uClassify | node module for accessing uclassify api | Natural Language Processing library

Topic Model approaches (Topic Extraction) are unsupervised approaches. So, you don't need to know that each document belongs to what categories (classes) [https://en.wikipedia.org/wiki/Topic_model]. Latent Dirichlet allocation (LDA) is a method for Topic Modeling. LDA divides the documents into topics and assigns a name to the topics. [https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation] Topic Model needs the number of output clusters as the same as clustering methods. But they assign a topic name to each output cluster. In contrast to Topic Model approaches, Document Classification approaches (Categorization) are supervised. So, they need the class labels. [https://en.wikipedia.org/wiki/Document_classification]