Topikointi

Topikointi is the Finnish term for topic modeling, a family of unsupervised learning techniques used to uncover latent topics in a collection of texts. In a typical model, each document is represented as a probabilistic mixture of topics, and each topic is a probability distribution over words. The output is a compact, interpretable representation of large text corpora that supports exploration, indexing, and analysis.

Common methods include Latent Dirichlet Allocation (LDA), which models documents as mixtures of topics and topics as mixtures of words; Non-negative Matrix Factorization (NMF) and Latent Semantic Analysis (LSA) as alternative approaches. The choice of method, the number of topics K, and the preprocessing pipeline influence results.

Process: collect a corpus, preprocess the text (tokenization, normalization, stop-word removal, stemming or lemmatization). Build a document-term matrix and train a topic model with a chosen K. The model yields topic-word distributions and document-topic distributions. Analysts often examine the top words for each topic and may assign human-readable labels. Results can be used to index documents, improve search, visualize topic prevalence over time, or cluster related documents.

Evaluation and limitations: topic quality is typically assessed with coherence scores or perplexity, but human judgment remains important. Topics are abstractions and can be sensitive to corpus size, domain, and preprocessing choices. Interpreting topics can be subjective, and choosing the number of topics is not always straightforward.

Applications: digital libraries, news aggregators, social media analytics, customer feedback analysis, and any scenario requiring scalable text understanding.