مقایسهٔ روشها
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| تحلیل فراوانی متن× | تنوع واژگانی× | تحلیل احساسات× | |
|---|---|---|---|
| حوزه | متنکاوی | متنکاوی | متنکاوی |
| خانواده | Process / pipeline | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1949 | — | — |
| پدیدآور≠ | George K. Zipf (frequency-distribution foundation) | — | — |
| نوع≠ | Descriptive text-mining analysis | Text quantification / lexical richness measurement | NLP text-classification task |
| منبع بنیادین≠ | Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley. link ↗ | 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. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| نامهای دیگر | word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | opinion mining, polarity detection, duygu analizi |
| مرتبط≠ | 4 | 3 | 3 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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