Text Sentiment Analysis based on BERT and Convolutional Neural Networks

Ping Huang, Huijuan Zhu, Lei Zheng, Ying Wang
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引用次数: 3

Abstract

The rapid development of the network has accelerated the speed of information circulation. Analyzing the emotional tendency contained in the network text is very helpful to tap the needs of users. However, most of the existing sentiment classification models rely on manually labeled text features, resulting in insufficient mining of deep semantic features hidden in the text, and it is difficult to improve the classification performance significantly. This paper presents a text sentiment classification model combining BERT and convolutional neural networks (CNN). The model uses BERT to complete the word embedding of the text, and then uses CNN to learn the deep semantic information about the text, so as mine the emotional tendency towards the text. Through verification on the large movie review dataset, BERT-CNN model can achieve an accuracy of 86.67%, which is significantly better than traditional classification method of textCNN. The results show that the method has good performance in this field.
基于BERT和卷积神经网络的文本情感分析
网络的快速发展加快了信息流通的速度。分析网络文本中蕴含的情感倾向,有助于挖掘用户的需求。然而,现有的情感分类模型大多依赖于人工标注的文本特征,导致对隐藏在文本中的深层语义特征挖掘不足,难以显著提高分类性能。本文提出了一种结合BERT和卷积神经网络(CNN)的文本情感分类模型。该模型使用BERT完成文本的词嵌入,然后使用CNN学习文本的深层语义信息,从而挖掘对文本的情感倾向。通过在大型影评数据集上的验证,BERT-CNN模型可以达到86.67%的准确率,明显优于传统的textCNN分类方法。结果表明,该方法在该领域具有良好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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