Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| N-gramu valodu modelis× | Sentimentu analīze× | |
|---|---|---|
| Nozare | Teksta ieguve | Teksta ieguve |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads | — | — |
| Autors | — | — |
| Tips≠ | Statistical language model | NLP text-classification task |
| Pirmavots≠ | Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Citi nosaukumi | n-gram model, statistical language model, N-gram Dil Modeli | opinion mining, polarity detection, duygu analizi |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition. | 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. |
| ScholarGateDatu kopa ↗ |
|
|