Sentiment classification aims to solve the problem of automatic judgment of sentiment polarity. In the sentiment classification task of text data, such as online reviews, traditional deep learning models are dedicated to algorithm optimization but ignore the characteristics of imbalanced distribution of the number of classified samples and the inclusion of weak tagging information such as ratings and tags. Based on the traditional deep learning model, the method of random oversampling and cost sensitivity is used to increase the contribution of a minority of samples to the model loss function and avoid the model biasing to the majority of samples. The model training is divided into two stages. In the first stage, a large amount of weak tagging data is used to train the model, therefore a model that captures the sentiment semantics of the data is obtained. After that, the model parameters trained in the first stage are used as the initial parameters of the second stage model training, and only a small amount of tagging data is used to continue training the model to reduce the impact of noise, thus reducing the use of manual tagging samples. The experimental results show that the method is considerably better than traditional deep learning models in the sentiment classification task of hotel review data.

中文翻译：

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基于弱标签信息和不平衡数据的情感分类

情感分类旨在解决情感极性自动判断的问题。在文本数据的情感分类任务（例如，在线评论）中，传统的深度学习模型专用于算法优化，但忽略了分类样本数量分布不均衡的特征，并包括诸如评分和标签之类的弱标签信息。在传统的深度学习模型的基础上，使用随机过采样和成本敏感性的方法来增加少数样本对模型损失函数的贡献，并避免模型对多数样本产生偏见。模型训练分为两个阶段。在第一阶段，大量的弱标记数据用于训练模型，因此，获得了一个捕获数据情感语义的模型。此后，将第一阶段训练的模型参数用作第二阶段模型训练的初始参数，并且仅使用少量标记数据来继续训练模型以减少噪声的影响，从而减少了使用手动标记样本。实验结果表明，该方法在酒店评论数据的情感分类任务中比传统的深度学习模型好得多。