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| Analisis Sentimen Berbasis Aspek (ABSA)× | Pemodelan Topik× | |
|---|---|---|
| Bidang≠ | Penambangan Teks | Pembelajaran Mendalam |
| Keluarga≠ | Process / pipeline | Machine learning |
| Tahun asal≠ | 2014 | 1999–2003 |
| Pencetus≠ | Pontiki et al. (SemEval-2014 Task 4) | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Tipe≠ | NLP fine-grained opinion-mining task | Unsupervised generative probabilistic model |
| Sumber perintis≠ | Pontiki, M. et al. (2014). SemEval-2014 Task 4: Aspect Based Sentiment Analysis. Proceedings of SemEval 2014, 27-35. DOI ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Alias | ABSA, aspect-level sentiment analysis, feature-based sentiment analysis, Konu Bazlı Duygu Analizi (ABSA) | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Terkait≠ | 4 | 5 |
| Ringkasan≠ | Aspect-based sentiment analysis (ABSA) is a fine-grained natural-language-processing task that detects sentiment separately for each aspect or feature mentioned in a text — such as a product's quality, price, or service — rather than scoring the document as a whole. It was consolidated as a shared task by Pontiki et al. in SemEval-2014 Task 4. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
| ScholarGateSet data ↗ |
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