পদ্ধতির তুলনা করুন

	TF-IDF	অনুভূতি বিশ্লেষণ	Text Classification	Word2Vec
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1988	—	—	2013
প্রবর্তক	Salton & Buckley	—	—	Tomas Mikolov et al.
ধরন	Text vectorization / term-weighting scheme	NLP text-classification task	Supervised NLP classification task	Neural word-embedding model
মৌলিক উৎস	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
অপর নাম	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
সম্পর্কিত	3	3	4	4
সারসংক্ষেপ	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.	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.