Sammenlign metoder

	Morfologisk analyse	Sentimentanalyse	TF-IDF
Fagområde	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	1980	—	1988
Ophavsperson	M.F. Porter (Porter stemmer)	—	Salton & Buckley
Type	Text-normalisation preprocessing task	NLP text-classification task	Text vectorization / term-weighting scheme
Oprindelig kilde	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	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.
Aliasser	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relaterede	4	3	3
Resumé	Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.	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.