Linganisha mbinu

	NLP ya Mitandao ya Kijamii	Uchanganuzi wa Hisia	TF-IDF
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2017	—	1988
Mwanzilishi	Community-established benchmark (SemEval shared tasks, Cardiff NLP group)	—	Salton & Buckley
Aina	NLP process pipeline for short, noisy social-media text	NLP text-classification task	Text vectorization / term-weighting scheme
Chanzo asilia	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.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Majina mbadala	Sosyal Medya Metin Analizi, social media text mining, Twitter NLP, short-text NLP	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Zinazohusiana	5	3	3
Muhtasari	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).	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.