Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Текстовый сетевой анализ× | Анализ тональности× | |
|---|---|---|
| Область | Интеллектуальный анализ текста | Интеллектуальный анализ текста |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2011 (Paranyushkin); 2005 (Diesner & Carley) | — |
| Автор метода≠ | Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley | — |
| Тип≠ | Text-mining network method | NLP text-classification task |
| Основополагающий источник≠ | Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Другие названия | semantic network analysis, word co-occurrence network, Metin Ağ Analizi (Text Network Analysis) | opinion mining, polarity detection, duygu analizi |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text. | 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 данных ↗ |
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