Salīdzināt metodes

	Stance Detection	BERT Embeddings	Viltus ziņu noteikšana	Sentimentu analīze	Tekstu klasifikācija
Nozare	Teksta ieguve	Teksta ieguve	Teksta ieguve	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Izcelsmes gads	2016	2019	—	—	—
Autors	Mohammad et al. (SemEval-2016 Task 6)	Devlin, Chang, Lee & Toutanova (Google AI)	—	—	—
Tips	NLP text-classification task toward a target	Contextual transformer text-representation method	NLP text-classification task	NLP text-classification task	Supervised NLP classification task
Pirmavots	Mohammad, S. et al. (2016). SemEval-2016 Task 6: Detecting Stance in Tweets. Proceedings of SemEval-2016, 31-41.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.	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.
Citi nosaukumi	stance classification, stance identification, Tutum Tespiti (Stance Detection)	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Saistītās	4	4	4	3	4
Kopsavilkums	Stance detection is a natural-language-processing task that decides the position a text takes toward a specific claim, event, or topic — labelling it as favor, against, or neutral. Formalised by Mohammad et al. in the SemEval-2016 Task 6 shared task, it differs from plain sentiment analysis because the label is always relative to a defined target rather than the overall emotional tone of the text.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	Fake news detection is a natural-language-processing classification task that assesses the credibility of news text and labels content as fake or genuine. Building on the social-media framing of Shu et al. (2017) and the automated-fact-checking framing of Thorne and Vlachos (2018), it turns unstructured news articles into a supervised credibility decision learned from labelled examples.	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.