Compară metode

	Analiza de sentiment bazată pe lexicon	Detectarea subiectivității
Domeniu	Mineritul textelor	Mineritul textelor
Familie	Process / pipeline	Process / pipeline
Anul apariției	—	—
Autorul original	—	—
Tip	Lexicon-based NLP sentiment-scoring task	NLP text-classification task
Sursa seminală	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'.	Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.
Denumiri alternative	dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi	subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection)
Înrudite	3	3
Rezumat	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.	Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis.