مقایسهٔ روش‌ها

	Doc2Vec	تحلیل احساسات	TF-IDF
حوزه	متن‌کاوی	متن‌کاوی	متن‌کاوی
خانواده	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	2014	—	1988
پدیدآور	Quoc V. Le & Tomas Mikolov	—	Salton & Buckley
نوع	Document-embedding representation learning	NLP text-classification task	Text vectorization / term-weighting scheme
منبع بنیادین	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	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.
نام‌های دیگر	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
مرتبط	4	3	3
خلاصه	Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.	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.