School of Computer and Communication of the Lanzhou University of Technology, Lanzhou City, Gansu Province, China.
PLoS One. 2021 Mar 5;16(3):e0247984. doi: 10.1371/journal.pone.0247984. eCollection 2021.
With the rapid development of the mobile internet, people are becoming more dependent on the internet to express their comments on products or stores; meanwhile, text sentiment classification of these comments has become a research hotspot. In existing methods, it is fairly popular to apply a deep learning method to the text classification task. Aiming at solving information loss, weak context and other problems, this paper makes an improvement based on the transformer model to reduce the difficulty of model training and training time cost and achieve higher overall model recall and accuracy in text sentiment classification. The transformer model replaces the traditional convolutional neural network (CNN) and the recurrent neural network (RNN) and is fully based on the attention mechanism; therefore, the transformer model effectively improves the training speed and reduces training difficulty. This paper selects e-commerce reviews as research objects and applies deep learning theory. First, the text is preprocessed by word vectorization. Then the IN standardized method and the GELUs activation function are applied based on the original model to analyze the emotional tendencies of online users towards stores or products. The experimental results show that our method improves by 9.71%, 6.05%, 5.58% and 5.12% in terms of recall and approaches the peak level of the F1 value in the test model by comparing BiLSTM, Naive Bayesian Model, the serial BiLSTM_CNN model and BiLSTM with an attention mechanism model. Therefore, this finding proves that our method can be used to improve the text sentiment classification accuracy and effectively apply the method to text classification.
随着移动互联网的飞速发展，人们越来越依赖互联网来表达对产品或商店的评价；同时，这些评论的文本情感分类已成为研究热点。在现有的方法中，应用深度学习方法来进行文本分类任务相当流行。针对信息丢失、上下文较弱等问题，本文基于变形金刚模型进行了改进，以降低模型训练难度和训练时间成本，在文本情感分类中实现更高的整体模型召回率和准确率。变形金刚模型取代了传统的卷积神经网络（CNN）和循环神经网络（RNN），完全基于注意力机制；因此，变形金刚模型有效地提高了训练速度，降低了训练难度。本文选择电子商务评论作为研究对象，并应用深度学习理论。首先，通过词向量对文本进行预处理。然后，在原始模型的基础上应用 IN 标准化方法和 GELUs 激活函数，分析在线用户对商店或产品的情感倾向。实验结果表明，我们的方法在召回率方面提高了 9.71%、6.05%、5.58%和 5.12%，并且通过比较 BiLSTM、朴素贝叶斯模型、串行 BiLSTM_CNN 模型和具有注意力机制的 BiLSTM 模型，在测试模型中接近 F1 值的峰值水平。因此，这一发现证明了我们的方法可以用于提高文本情感分类的准确性，并有效地将该方法应用于文本分类。
