Semi-Supervised Learning vs. Few-Shot Learning: Which is Better for Sentiment Analysis on Hotel Reviews Towards a Small Labeled Training Data?

The massive growth in user reviews on the online travel agent (OTA) website can be automatically processed using sentiment analysis to understand consumer satisfaction or feedback. Sentiment analysis is commonly implemented as a sentiment classification task by applying classical machine learning and deep learning algorithms. However, implementing both strategies has a significant challenge in providing a reliable labeled dataset since labeling is time-consuming and highly resource-intensive. Therefore, this study aims to compare the performance of two learning methods: semi-supervised learning (SSL) and few-shot learning (FSL), since there is still no direct, controlled comparison between both methods. SSL is a learning method that builds a generalized classification model as a refined model based on automatically generated additional labeled data. In contrast, FSL is a learning method that enables a generalized pre-trained model to predict unlabeled data using only a few labeled samples per class. This study evaluates the self-training method on SSL, and the implemented FSL algorithm is Sentence Transformer Fine-Tuning (SETFIT). The results show that implementing FSL (employing only 16 labeled training samples) outperforms SSL with an accuracy improvement of 9.5%. The implementation of SETFIT is very promising as a solution to overcome the limited amount of labeled data in the classification task. Moreover, SETFIT is more adaptable to various low-resource language domains than other, more data-intensive learning approaches.

@article{Kusumaningrum2025,
  title     = {Semi-Supervised Learning vs. Few-Shot Learning: Which is Better for Sentiment Analysis on Hotel Reviews Towards a Small Labeled Training Data?},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Retno Kusumaningrum and Ahmad Ainun Herlambang and Wafiq Afifah and Adi Wibowo and Sutikno and Priyo Sidik Sasongko},
  doi       = {10.14569/IJACSA.2025.0161168},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161168}
}