Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Analisis Sentimen× | Segmentasi Teks× | |
|---|---|---|
| Bidang | Perlombongan Teks | Perlombongan Teks |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | — | 1997 |
| Pengasas≠ | — | Marti A. Hearst (TextTiling) |
| Jenis≠ | NLP text-classification task | NLP document-structure / topic-boundary detection |
| Sumber perintis≠ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ | Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64. link ↗ |
| Alias≠ | opinion mining, polarity detection, duygu analizi | topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation) |
| Berkaitan≠ | 3 | 4 |
| Ringkasan≠ | 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. | Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text. |
| ScholarGateSet data ↗ |
|
|