Methoden vergelijken

	Sentimentanalyse op basis van lexicons	Tekstanalyse van complexiteit
Vakgebied	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	—	—
Grondlegger	—	—
Type	Lexicon-based NLP sentiment-scoring task	Linguistic-feature measurement pipeline
Oorspronkelijke bron	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.
Aliassen	dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi	readability analysis, linguistic complexity assessment, Metin Karmaşıklığı Analizi
Verwant	3	2
Samenvatting	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.