Сравнение методов

	Обработка естественного языка (NLP) в социальных сетях	Тематическое моделирование
Область	Интеллектуальный анализ текста	Глубокое обучение
Семейство	Process / pipeline	Machine learning
Год появления	2017	1999–2003
Автор метода	Community-established benchmark (SemEval shared tasks, Cardiff NLP group)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Тип	NLP process pipeline for short, noisy social-media text	Unsupervised generative probabilistic model
Основополагающий источник	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Другие названия	Sosyal Medya Metin Analizi, social media text mining, Twitter NLP, short-text NLP	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Связанные	5	5
Сводка	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).	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.