השוואת שיטות

	ניתוח סנטימנט	מידול נושאים
תחום	כריית טקסט	למידה עמוקה
משפחה	Process / pipeline	Machine learning
שנת המקור	—	1999–2003
הוגה השיטה	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
סוג	NLP text-classification task	Unsupervised generative probabilistic model
מקור מכונן	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.
כינויים	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
קשורות	3	5
תקציר	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.