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| Класификация на текст× | Анализ на настроенията× | |
|---|---|---|
| Област | Извличане на текст | Извличане на текст |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване | — | — |
| Създател | — | — |
| Тип≠ | Supervised NLP classification task | NLP text-classification task |
| Основополагащ източник≠ | Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Други названия≠ | text categorization, document classification, topic classification, metin sınıflandırma | opinion mining, polarity detection, duygu analizi |
| Свързани≠ | 4 | 3 |
| Резюме≠ | Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples. | 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. |
| ScholarGateНабор от данни ↗ |
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