Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise de Coocorrência× | Análise de Sentimento× | |
|---|---|---|
| Área | Mineração de texto | Mineração de texto |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1957 | — |
| Autor original≠ | J.R. Firth (distributional principle) | — |
| Tipo≠ | Text-mining / distributional-semantics technique | NLP text-classification task |
| Fonte seminal≠ | Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Outros nomes | word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi | opinion mining, polarity detection, duygu analizi |
| Relacionados≠ | 4 | 3 |
| Resumo≠ | Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps. | 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. |
| ScholarGateConjunto de dados ↗ |
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