Σύγκριση μεθόδων

	Επεξεργασία Φυσικής Γλώσσας (NLP) για Κοινωνικά Δίκτυα	Ταξινόμηση Κειμένου
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017	—
Δημιουργός	Community-established benchmark (SemEval shared tasks, Cardiff NLP group)	—
Τύπος	NLP process pipeline for short, noisy social-media text	Supervised NLP classification task
Θεμελιώδης πηγή	Rosenthal, S. et al. (2017). SemEval-2017 Task 4: Sentiment Analysis in Twitter. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). ACL.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Εναλλακτικές ονομασίες	Sosyal Medya Metin Analizi, social media text mining, Twitter NLP, short-text NLP	text categorization, document classification, topic classification, metin sınıflandırma
Συναφείς	5	4
Σύνοψη	Social Media NLP is a specialised natural-language-processing pipeline designed for the short, noisy, and informal text that appears on platforms such as Twitter, Reddit, and comment sections. Unlike general-purpose NLP, this pipeline accounts for platform-specific conventions — hashtags, emojis, abbreviations, and code-switching — enabling tasks such as hashtag analysis, viral content detection, and public-opinion measurement. The benchmark tradition for this approach was established through the SemEval-2017 Task 4 shared task (Rosenthal et al., 2017) and the TweetEval unified benchmark (Barbieri et al., 2020).	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.