Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912073350381568 |
|---|---|
| author | Jafarlou, Minoo Kubek, Mario M. |
| author_facet | Jafarlou, Minoo Kubek, Mario M. |
| contents | Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and time. This research, however, leverages an efficient answer. By exploring label propagation in semi-supervised learning, we can significantly reduce the number of labels required compared to traditional methods. We employ a transductive label propagation method based on the manifold assumption for text classification. Our approach utilizes a graph-based method to generate pseudo-labels for unlabeled data for the text classification task, which are then used to train deep neural networks. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning, thereby reducing labeling costs. Based on previous successes in other domains, this study builds and evaluates this approach's effectiveness in sentiment analysis, presenting insights into semi-supervised learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11355 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning Jafarlou, Minoo Kubek, Mario M. Machine Learning Artificial Intelligence Computation and Language Information Retrieval 68T50, 68T07 I.2.6; I.2.7; H.3.3 Labeling datasets is a noteworthy challenge in machine learning, both in terms of cost and time. This research, however, leverages an efficient answer. By exploring label propagation in semi-supervised learning, we can significantly reduce the number of labels required compared to traditional methods. We employ a transductive label propagation method based on the manifold assumption for text classification. Our approach utilizes a graph-based method to generate pseudo-labels for unlabeled data for the text classification task, which are then used to train deep neural networks. By extending labels based on cosine proximity within a nearest neighbor graph from network embeddings, we combine unlabeled data into supervised learning, thereby reducing labeling costs. Based on previous successes in other domains, this study builds and evaluates this approach's effectiveness in sentiment analysis, presenting insights into semi-supervised learning. |
| title | Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning |
| topic | Machine Learning Artificial Intelligence Computation and Language Information Retrieval 68T50, 68T07 I.2.6; I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2410.11355 |