পদ্ধতির তুলনা করুন

	মতামত খনন	অনুভূতি বিশ্লেষণ	Text Classification
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2012	—	—
প্রবর্তক	Bing Liu	—	—
ধরন	NLP information-extraction task	NLP text-classification task	Supervised NLP classification task
মৌলিক উৎস	Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool.	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.
অপর নাম	aspect-based sentiment analysis, opinion extraction, Görüş Madenciliği (Opinion Mining)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
সম্পর্কিত	3	3	4
সারসংক্ষেপ	Opinion mining is a natural-language-processing task that systematically extracts and analyses user opinions about a product, service, or topic — identifying the specific features (aspects) being discussed, the sentiment expressed toward each, and the opinion holders. Consolidated by Bing Liu (2012), it goes beyond a single document-level label to produce structured aspect–opinion–holder records.	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.