পদ্ধতির তুলনা করুন

	শব্দতাত্ত্বিক বিশ্লেষণ	অনুভূতি বিশ্লেষণ
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1980	—
প্রবর্তক	M.F. Porter (Porter stemmer)	—
ধরন	Text-normalisation preprocessing task	NLP text-classification task
মৌলিক উৎস	Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
অপর নাম	stemming, lemmatization, Morfolojik Analiz ve Kök Bulma	opinion mining, polarity detection, duygu analizi
সম্পর্কিত	4	3
সারসংক্ষেপ	Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic.	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.