Summary of Reducing Labeling Costs in Sentiment Analysis Via Semi-supervised Learning, by Minoo Jafarlou and Mario M. Kubek
Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
by Minoo Jafarlou, Mario M. Kubek
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research tackles the problem of labeling datasets efficiently, reducing the need for costly and time-consuming traditional methods. By exploring label propagation in semi-supervised learning, the authors develop a transductive method that leverages the manifold assumption for text classification. The approach uses graph-based pseudo-labeling to train deep neural networks with unlabeled data, reducing labeling costs by combining them into supervised learning. The study evaluates this approach’s effectiveness in sentiment analysis, presenting insights into semi-supervised learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a clever way to make machine learning work more efficiently. Right now, it takes a lot of effort and money to label datasets for training models. This research shows how to use some unlabelled data to help with that process, making it faster and cheaper. The scientists developed a new method that uses a special kind of map (called the manifold assumption) to figure out what labels should be for text classification. They tested this approach on sentiment analysis and found it worked well. |
Keywords
» Artificial intelligence » Machine learning » Semi supervised » Supervised » Text classification
