Salīdzināt metodes

	Daudzvalodu tekstu analīze	Sentimentu analīze	Tēmu modelēšana
Nozare	Teksta ieguve	Teksta ieguve	Dziļā mācīšanās
Saime	Process / pipeline	Process / pipeline	Machine learning
Izcelsmes gads	—	—	1999–2003
Autors	—	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Tips	Multilingual NLP representation task	NLP text-classification task	Unsupervised generative probabilistic model
Pirmavots	Conneau, A. et al. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL.	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.
Citi nosaukumi	multilingual text analysis, cross-lingual representation learning, Çok Dilli Metin Analizi (Cross-lingual)	opinion mining, polarity detection, duygu analizi	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Saistītās	4	3	5
Kopsavilkums	Cross-lingual text analysis lets you compare and analyse texts written in different languages within a shared vector space. Building on multilingual representation learning surveyed by Conneau et al. (2020) and Pires et al. (2019), it maps documents from several languages into one common embedding space so multilingual corpora can be studied together.	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.