Bandingkan kaedah

	Analisis Frekuensi Teks	Kepelbagaian Leksikal	Analisis Sentimen
Bidang	Perlombongan Teks	Perlombongan Teks	Perlombongan Teks
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1949	—	—
Pengasas	George K. Zipf (frequency-distribution foundation)	—	—
Jenis	Descriptive text-mining analysis	Text quantification / lexical richness measurement	NLP text-classification task
Sumber perintis	Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley.	McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Alias	word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	opinion mining, polarity detection, duygu analizi
Berkaitan	4	3	3
Ringkasan	Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis.	Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures.	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.