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Nøkkelordekstraksjon×NMF emnemodellering×Sentimentanalyse×
FagfeltTekstutvinningTekstutvinningTekstutvinning
FamilieProcess / pipelineProcess / pipelineProcess / pipeline
Opprinnelsesår1999
OpphavspersonLee & Seung
TypeNLP text-mining taskMatrix-factorization topic modelNLP text-classification task
Opprinnelig kildeMihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411. link ↗Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791. DOI ↗Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗
Aliaskeyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMFopinion mining, polarity detection, duygu analizi
Relaterte443
SammendragKeyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.
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ScholarGateSammenlign metoder: Keyword Extraction · NMF Topic Modeling · Sentiment Analysis. Hentet 2026-06-18 fra https://scholargate.app/no/compare