Võrdle meetodeid
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| Leksikonipõhine tundeanalüüs× | Subjektiivsuse tuvastamine× | |
|---|---|---|
| Valdkond | Tekstikaeve | Tekstikaeve |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta | — | — |
| Looja | — | — |
| Tüüp≠ | Lexicon-based NLP sentiment-scoring task | NLP text-classification task |
| Algallikas≠ | 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'. link ↗ | Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210. DOI ↗ |
| Rööpnimetused | dictionary-based sentiment analysis, rule-based sentiment scoring, Sözlük Tabanlı Duygu Analizi | subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection) |
| Seotud | 3 | 3 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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