قارن الطرق

	تحليل المشاعر	تكرار المصطلح - التردد العكسي لتكرار المصطلح
المجال	تنقيب النصوص	تنقيب النصوص
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	—	1988
صاحب الطريقة	—	Salton & Buckley
النوع	NLP text-classification task	Text vectorization / term-weighting scheme
المصدر التأسيسي	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	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
ذات صلة	3	3
الملخص	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.