Comparar métodos

	Análise Morfológica	Identificação de Idioma (LID)	Análise de Sentimento	Segmentação de texto
Área	Mineração de texto	Mineração de texto	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1980	—	—	1997
Autor original	M.F. Porter (Porter stemmer)	—	—	Marti A. Hearst (TextTiling)
Tipo	Text-normalisation preprocessing task	NLP text-classification task	NLP text-classification task	NLP document-structure / topic-boundary detection
Fonte seminal	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.	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.
Outros nomes	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	language detection, LID, Dil Tanımlama (Language Identification)	opinion mining, polarity detection, duygu analizi	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)
Relacionados	4	4	3	4
Resumo	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.	Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.