Bandingkan kaedah

	Kesamaan Semantik	Sematik BERT	Analisis Sentimen	TF-IDF
Bidang	Perlombongan Teks	Perlombongan Teks	Perlombongan Teks	Perlombongan Teks
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	2019	2019	—	1988
Pengasas	Nils Reimers & Iryna Gurevych (Sentence-BERT)	Devlin, Chang, Lee & Toutanova (Google AI)	—	Salton & Buckley
Jenis	NLP text-comparison task	Contextual transformer text-representation method	NLP text-classification task	Text vectorization / term-weighting scheme
Sumber perintis	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alias	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Berkaitan	4	4	3	3
Ringkasan	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like 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.	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.