เปรียบเทียบวิธี

	การวิเคราะห์การปรากฏร่วมกัน	การสกัดคำสำคัญ	TF-IDF
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	1957	—	1988
ผู้ริเริ่ม	J.R. Firth (distributional principle)	—	Salton & Buckley
ประเภท	Text-mining / distributional-semantics technique	NLP text-mining task	Text vectorization / term-weighting scheme
แหล่งต้นตำรับ	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	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.
ชื่อเรียกอื่น	word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
ที่เกี่ยวข้อง	4	4	3
สรุป	Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.	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.