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	Analiza sentymentu oparta na leksykonach	Analiza sentymentu	Analiza złożoności tekstu
Dziedzina	Eksploracja tekstu	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline	Process / pipeline
Rok powstania	—	—	—
Twórca	—	—	—
Typ	Lexicon-based NLP sentiment-scoring task	NLP text-classification task	Linguistic-feature measurement pipeline
Źródło pierwotne	Nielsen, F.Å. (2011). A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs. Proceedings of the ESWC Workshop on 'Making Sense of Microposts'.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Vajjala, S. & Meurers, D. (2014). Readability Assessment for Text Simplification: From Analysing Documents to Identifying Sentential Simplifications. International Journal of Applied Linguistics, 165(2), 194-222.
Inne nazwy	dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi	opinion mining, polarity detection, duygu analizi	readability analysis, linguistic complexity assessment, Metin Karmaşıklığı Analizi
Pokrewne	3	3	2
Podsumowanie	Lexicon-based sentiment analysis computes sentiment at the word level using prebuilt sentiment dictionaries such as AFINN (Nielsen, 2011), SentiWordNet, VADER (Hutto & Gilbert, 2014), and the NRC Emotion Lexicon. It scores text by looking words up in a dictionary of charged terms, so it requires no labelled training data.	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 complexity analysis measures the linguistic difficulty of a text along dimensions such as syntactic complexity (sentence length, embedded clauses), lexical density, and referential chains. Grounded in readability research consolidated by Vajjala and Meurers (2014) and Crossley and colleagues (2011), it turns prose into quantitative scores that estimate how hard a document is to read.