Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Orsanalys× | Nyckelordsutvinning× | Sentimentanalys× | |
|---|---|---|---|
| Ämnesområde | Textutvinning | Textutvinning | Textutvinning |
| Familj | Process / pipeline | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1957 | — | — |
| Upphovsperson≠ | J.R. Firth (distributional principle) | — | — |
| Typ≠ | Text-mining / distributional-semantics technique | NLP text-mining task | NLP text-classification task |
| Ursprungskälla≠ | Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell. link ↗ | Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias | word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi | keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction) | opinion mining, polarity detection, duygu analizi |
| Närliggande≠ | 4 | 4 | 3 |
| Sammanfattning≠ | 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. | Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020). | 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. |
| ScholarGateDatamängd ↗ |
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