Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Analiza sentimentelor× | Modelarea tematică× | |
|---|---|---|
| Domeniu≠ | Mineritul textelor | Învățare profundă |
| Familie≠ | Process / pipeline | Machine learning |
| Anul apariției≠ | — | 1999–2003 |
| Autorul original≠ | — | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Tip≠ | NLP text-classification task | Unsupervised generative probabilistic model |
| Sursa seminală≠ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Denumiri alternative≠ | opinion mining, polarity detection, duygu analizi | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | 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. |
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