قارن الطرق

	تضمينات GloVe	تحليل المشاعر	تكرار المصطلح - التردد العكسي لتكرار المصطلح
المجال	تنقيب النصوص	تنقيب النصوص	تنقيب النصوص
العائلة	Process / pipeline	Process / pipeline	Process / pipeline
سنة النشأة	2014	—	1988
صاحب الطريقة	Pennington, Socher & Manning	—	Salton & Buckley
النوع	Static word-embedding model	NLP text-classification task	Text vectorization / term-weighting scheme
المصدر التأسيسي	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	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.
الأسماء البديلة	GloVe, global vectors, GloVe Kelime Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
ذات صلة	3	3	3
الملخص	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.	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.