Uporedite metode

	Detekcija lažnih vesti	BERT Embeddings	Analiza sentimenta	Klasifikacija teksta
Oblast	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Porodica	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	—	2019	—	—
Tvorac	—	Devlin, Chang, Lee & Toutanova (Google AI)	—	—
Tip	NLP text-classification task	Contextual transformer text-representation method	NLP text-classification task	Supervised NLP classification task
Temeljni izvor	Shu, K. et al. (2017). Fake News Detection on Social Media. ACM SIGKDD.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	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.
Drugi nazivi	misinformation detection, false news classification, automated fact checking, Yanlış/Sahte Haber Tespiti	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Srodne	4	4	3	4
Sažetak	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.	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.	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.