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Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning — arXiv:2610.41518

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41518
License (as archived): CC0-1.0
Archived: 2026-11-03T08:05:42.048Z
SHA-256 of the archived text: 3b86e9a971463cd3a71c5d47819c1fc63caeaa72c0c3f393de04105a10e4ce95
Stable URL of this page: https://metraipolis.com/documento/dc_ff2db2910ced

How to cite

Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning — arXiv:2610.41518 — arXiv (en), https://arxiv.org/abs/2610.41518 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff2db2910ced (Metraipolis archive dc_ff2db2910ced, sha256 3b86e9a971463cd3a71c5d47819c1fc63caeaa72c0c3f393de04105a10e4ce95)

BibTeX

@misc{dcff2db2910ced,
  title = {Enhancing Sentiment Classification on Small Datasets through Data Augmentation and Transfer Learning — arXiv:2610.41518},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41518},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff2db2910ced; sha256 3b86e9a971463cd3a71c5d47819c1fc63caeaa72c0c3f393de04105a10e4ce95},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41518. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Small-scale sentiment classification often suffers from data scarcity, which limits the generalization ability of the models. This study evaluates and compares the effectiveness of three data augmentation strategies: Easy Data Augmentation (EDA), back-translation, and contextual token substitution (nlpaug-style), with both traditional machine learning classifiers (Logistic Regression, Random Forest) and transformer-based models (BERT). We perform a comprehensive empirical comparison with low-resource sentiment datasets by summarizing the results of recent studies and performing targeted head-to-head experiments. Our findings indicate that all augmentation methods improve performance. Contextual augmentation yields the most consistent gains for BERT models, while EDA and back-translation provide greater benefits for traditional classifiers. These insights help guide the selection of data augmentation techniques tailored to model type and dataset size, filling a critical gap in research on data augmentation for sentiment classification on small datasets.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.41518 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:59:53.622Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).