# Semi-supervised and Transfer learning approaches for low resource sentiment classification

arXiv: https://arxiv.org/abs/1806.02863 · HTML: https://mlsanity.com/paper/1806.02863/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2018.

## Built on by (most cited first, 5 of 5)

- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages](https://mlsanity.com/paper/2111.10047/) (2021)
- [RideKE: Leveraging Low-Resource, User-Generated Twitter Content for Sentiment and Emotion Detection in Kenyan Code-Switched Dataset](https://mlsanity.com/paper/2502.06180/) (2025)
- [Transfer Learning for Low-Resource Sentiment Analysis](https://mlsanity.com/paper/2304.04703/) (2023)
- [An information-Theoretic Approach to Semi-supervised Transfer Learning](https://mlsanity.com/paper/2306.06731/) (2023)

## Abstract (arXiv)

Sentiment classification involves quantifying the affective reaction of a human to a document, media item or an event. Although researchers have investigated several methods to reliably infer sentiment from lexical, speech and body language cues, training a model with a small set of labeled datasets is still a challenge. For instance, in expanding sentiment analysis to new languages and cultures, it may not always be possible to obtain comprehensive labeled datasets. In this paper, we investigate the application of semi-supervised and transfer learning methods to improve performances on low resource sentiment classification tasks. We experiment with extracting dense feature representations, pre-training and manifold regularization in enhancing the performance of sentiment classification systems. Our goal is a coherent implementation of these methods and we evaluate the gains achieved by these methods in matched setting involving training and testing on a single corpus setting as well as two cross corpora settings. In both the cases, our experiments demonstrate that the proposed methods can significantly enhance the model performance against a purely supervised approach, particularly in cases involving a handful of training data.