Again, the rationale is that if tokens often appear together in documents, they are probably related to one another and, hence, stem from the same topic. Moreover, the model learns how different tokens are related to one another. Documents which deal with the same set of tokens, i.e., tokens that come from the same topic, are classified as being similar. In our examples, we deal with so-called topic models, models that are organizing the documents with regard to their content. Unsupervised Learning, on the other hand, implies that the machine is fed with a corpus of different documents and asked to find a new organization for them based on their content. Dependent variables are in our examples sentiment or party affiliation. In the examples we will showcase, the features are the tokens that are contained in a document. Supervised means that we will need to “show” the machine a data set that already contains the value or label we want to predict (the “dependent variable”) as well as all the variables that are used to predict the class/value (the independent variables or, in ML lingo, features). In the following script, we will introduce you to the supervised and unsupervised classification of text. 5.2 Unsupervised Learning: Topic Models. 5.1.10 Supervised ML with tidymodels in a nutshell.5.1.4 textrecipes – further preprocessing steps.3.4.2 Letting the scraper navigate on its own.2.2 Application Programming Interfaces (APIs).
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