Methoden vergleichen

	Morphologische Analyse	Sprachidentifikation (LID)	Sentiment-Analyse	TF-IDF
Fachgebiet	Text Mining	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	1980	—	—	1988
Urheber	M.F. Porter (Porter stemmer)	—	—	Salton & Buckley
Typ	Text-normalisation preprocessing task	NLP text-classification task	NLP text-classification task	Text vectorization / term-weighting scheme
Wegweisende Quelle	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	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.
Aliasnamen	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	language detection, LID, Dil Tanımlama (Language Identification)	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Verwandt	4	4	3	3
Zusammenfassung	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.	Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.	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.