เปรียบเทียบวิธี

	การจัดกลุ่มเอกสาร	การสกัดคำสำคัญ	ความคล้ายคลึงทางความหมาย	การวิเคราะห์ความรู้สึก
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	—	—	2019	—
ผู้ริเริ่ม	—	—	Nils Reimers & Iryna Gurevych (Sentence-BERT)	—
ประเภท	Unsupervised text-mining task	NLP text-mining task	NLP text-comparison task	NLP text-classification task
แหล่งต้นตำรับ	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
ชื่อเรียกอื่น	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	opinion mining, polarity detection, duygu analizi
ที่เกี่ยวข้อง	4	4	4	3
สรุป	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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).	Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.	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.