Sammenlign metoder

	Nøgleordsudtræk	Sentimentanalyse	Emne-modellering
Fagområde	Tekstmining	Tekstmining	Dyb læring
Familie	Process / pipeline	Process / pipeline	Machine learning
Oprindelsesår	—	—	1999–2003
Ophavsperson	—	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Type	NLP text-mining task	NLP text-classification task	Unsupervised generative probabilistic model
Oprindelig kilde	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Aliasser	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Relaterede	4	3	5
Resumé	Keyword 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).	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.