Tekstkategorisering

Tekstkategorisering, also known as text classification or text categorization, is the process of assigning predefined categories or labels to text documents. This is a fundamental task in natural language processing (NLP) and machine learning. The goal is to automatically organize and structure unstructured text data, making it more manageable and useful for various applications.

The process typically involves training a machine learning model on a dataset of text documents that have already been labeled with their correct categories. Once trained, the model can then predict the category of new, unseen text documents. Common algorithms used for text categorization include Naive Bayes, Support Vector Machines (SVMs), logistic regression, and more recently, deep learning models like recurrent neural networks (RNNs) and transformer networks.

Feature extraction is a crucial step in text categorization. This involves converting raw text into a numerical representation that machine learning algorithms can understand. Techniques like bag-of-words (BoW), TF-IDF (term frequency-inverse document frequency), and word embeddings (e.g., Word2Vec, GloVe) are commonly employed.

Applications of text categorization are widespread. They include spam detection in emails, sentiment analysis (classifying text as positive, negative, or neutral), topic modeling, news article categorization, content moderation, and document routing. The accuracy and effectiveness of text categorization depend heavily on the quality and quantity of training data, as well as the choice of algorithm and feature extraction methods.