Classification is the process of assigning a class label to a document. This is usually done through some sort of training process, where the system is first presented with a set of documents that have been manually classified, and then uses this information to automatically classify new documents.
Classification can be used for a variety of different tasks, such as topic classification, sentiment analysis, spam detection, and so on.
Classification is often used in conjunction with other text analytics tasks, such as clustering and topic modeling. Clustering is the process of grouping together similar documents, without necessarily knowing beforehand what the groups will be. Topic modeling is a related technique that tries to automatically discover the topics that are present in a collection of documents. Classification can be used to improve the accuracy of these other tasks by providing additional information about the documents.
Algorithm for Classification
The algorithm for classification can be either rule-based or statistical. Rule-based classification systems look for a set of pre-defined rules that can be used to classify documents. Statistical classification systems, on the other hand, build up a model of what each class looks like and then use this model to classify new documents. Statistical methods are generally more accurate than rule-based methods, but they can be more difficult to develop and understand.
Classification can help users navigate through documents by automatically assigning labels to them. These labels can then be used to filter or search for documents. For example, if you were looking for all the medical records in a database, you could use classification to automatically label all the documents as “medical” or “non-medical” and then filter out the non-medical ones.
Similarly, if you were trying to find all the documents about a specific topic, you could use classification to automatically label all the documents as “relevant” or “non-relevant” and then filter out the non-relevant ones.