重庆大学学报2024，Vol.47Issue(12)：100-113,14.DOI:10.11835/j.issn.1000-582X.2024.12.010
一种基于半监督的句子情感分类模型
A semi-supervised model for sentence-level sentiment classification
摘要
Abstract
Sentence sentiment classification is an important task for extracting emotional semantics from text.Currently,the best tools for sentence sentiment classification leverage deep neural networks,particularly BERT-based models.However,these models require large,high-quality labeled datasets to perform effectively.In practice,labeled data is usually limited,leading to overfitting on small datasets and difficulties in capturing subtle sentiment features.Although existing semi-supervised models utilize features from large unlabeled datasets,they still face challenges from errors introduced by pseudo-labeled samples.Additionally,once test data is labeled,these models often do not adapt by incorporating feature information from test data.To address these issues,this paper proposes a semi-supervised sentence sentiment classification model.First,a K-nearest neighbors-based weighting mechanism is designed,assigning higher weights to high confidence samples to minimize error propagation during parameter learning.Second,a two-stage training mechanism is implemented,enabling the model to correct misclassified samples in the test data.Extensive experiments on multiple datasets show that this method achieves strong performance on small datasets.关键词
句子情感分类/半监督学习/K-近邻/transformerKey words
sentence-level sentiment classification/semi-supervised learning/K-nearest neighbors/transformer分类
信息技术与安全科学引用本文复制引用
苏静,Murtadha Ahmed..一种基于半监督的句子情感分类模型[J].重庆大学学报,2024,47(12):100-113,14.基金项目
国家自然科学基金资助项目(62172335). Supported by the National Natural Science Foundation of China(62172335). （62172335）
