Bandingkan metode

	Ekstraksi Kata Kunci	TF-IDF	Pemodelan Topik
Bidang	Penambangan Teks	Penambangan Teks	Pembelajaran Mendalam
Keluarga	Process / pipeline	Process / pipeline	Machine learning
Tahun asal	—	1988	1999–2003
Pencetus	—	Salton & Buckley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tipe	NLP text-mining task	Text vectorization / term-weighting scheme	Unsupervised generative probabilistic model
Sumber perintis	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Terkait	4	3	5
Ringkasan	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).	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.	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.