Aiheluokittelu

Aiheluokittelu (Finnish for "topic classification") is the process of assigning one or more topic labels to a text document or other piece of content. It is a subfield of text classification and information retrieval that groups documents by subject matter to enable organization, search, filtering and analysis of large text corpora.

Common approaches to aiheluokittelu include rule-based systems, supervised machine learning, and unsupervised or semi-supervised techniques. Traditional supervised methods use feature representations such as bag-of-words, TF–IDF or n-grams with classifiers like logistic regression, support vector machines or decision trees. Modern approaches rely on dense vector embeddings and neural networks, including convolutional and transformer-based models, which can capture semantic and contextual information more effectively. Topic modeling methods such as latent Dirichlet allocation (LDA) are used for exploratory, unsupervised discovery of themes.

Evaluation typically employs metrics from classification tasks, for example accuracy, precision, recall and F1 score, with attention to multi-label scenarios and class imbalance. Practical challenges include short or noisy texts, polysemy and ambiguity, evolving topics, domain shifts and multilingual content. Addressing these often requires data augmentation, transfer learning, careful preprocessing and continuous model updating.

Applications of aiheluokittelu span news categorization, content recommendation, customer feedback analysis, moderation, digital libraries and academic research. The technique supports automated organization and insight extraction in both commercial and research settings.