เปรียบเทียบวิธี

	การวิเคราะห์ข้อความข้ามภาษา	การวิเคราะห์ความรู้สึก	การสร้างแบบจำลองหัวข้อ (Topic Modeling)
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ	การเรียนรู้เชิงลึก
ตระกูล	Process / pipeline	Process / pipeline	Machine learning
ปีกำเนิด	—	—	1999–2003
ผู้ริเริ่ม	—	—	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
ประเภท	Multilingual NLP representation task	NLP text-classification task	Unsupervised generative probabilistic model
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	4	3	5
สรุป	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.