szövegosztályozás

Szövegosztályozás, 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 with numerous applications. The goal is to automatically understand the content of a text and determine which category it belongs to.

The process typically involves training a machine learning model on a dataset of labeled text examples. For instance, a dataset might contain news articles labeled as "sports," "politics," or "technology." The model learns patterns and features within the text that are indicative of each category. Common features include word frequencies, the presence of specific keywords, sentence structure, and even semantic relationships between words.

Once trained, the model can then be used to classify new, unseen text documents. Applications of szövegosztályozás are widespread. It is used for spam detection in emails, sentiment analysis to gauge public opinion on social media, topic modeling to organize large collections of documents, and content moderation to filter inappropriate material.

Various algorithms can be employed for szövegosztályozás, including Naive Bayes, Support Vector Machines (SVMs), logistic regression, and more recently, deep learning models like recurrent neural networks (RNNs) and transformer networks. The choice of algorithm often depends on the size and complexity of the dataset, as well as the desired accuracy and computational resources available. The effectiveness of a szövegosztályozás system is usually evaluated using metrics such as precision, recall, and F1-score.