Sammenlign metoder

	Subjektivitetsdetektion	Sentimentanalyse	Tekstklassificering
Fagområde	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	—	—	—
Ophavsperson	—	—	—
Type	NLP text-classification task	NLP text-classification task	Supervised NLP classification task
Oprindelig kilde	Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasser	subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Relaterede	3	3	4
Resumé	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.	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 classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.