Comparer des méthodes

	Analyse de sentiment basée sur un lexique	Analyse de la complexité textuelle
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	—	—
Auteur d'origine	—	—
Type	Lexicon-based NLP sentiment-scoring task	Linguistic-feature measurement pipeline
Source fondatrice	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'.	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.
Alias	dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi	readability analysis, linguistic complexity assessment, Metin Karmaşıklığı Analizi
Apparentées	3	2
Résumé	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.	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.