Sentence classification deals with understanding text found in natural languages and determining the classes that it may belong to. In the text classification set of problems, you will have a set of documents d that belongs to the corpus X (which contains all the documents). You will also have a set of finite classes C = {c1 , c2, ..., cn}. Classes are also called categories or labels. To train a model, you would need a classifier, which is generally a well-tested algorithm (not necessary but in this case we will be talking about a well-tested algorithm that is used in fastText) and you will need a corpus with documents and associated labeling identifying the classes that each document belongs to.
Text classification has many practical uses, such as the following:
- Creating spam classifiers in email
- Page ranking and indexing in search engines
- Sentiment...