পদ্ধতির তুলনা করুন

	মূলশব্দ নিষ্কাশন	অনুভূতি বিশ্লেষণ	টপিক মডেলিং
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	গভীর শিখন
পরিবার	Process / pipeline	Process / pipeline	Machine learning
উদ্ভবের বছর	—	—	1999–2003
প্রবর্তক	—	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
ধরন	NLP text-mining task	NLP text-classification task	Unsupervised generative probabilistic model
মৌলিক উৎস	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
অপর নাম	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
সম্পর্কিত	4	3	5
সারসংক্ষেপ	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.