King's College London, London WC2R 2LS, UK.
Comput Intell Neurosci. 2022 Jul 6;2022:9034773. doi: 10.1155/2022/9034773. eCollection 2022.
In the context dominated by Internet communication, people's various emotions can be clearly reflected through network public opinion, whether it is the view of political affairs, the preference for entertainment, or the demand for life. This also allows management or providers to meet their needs more specifically. Based on today's need to understand the trend of Internet public opinion, this paper describes a deep neural network (DNN). A deep neural network is a machine learning model that is the foundation of deep learning and has a strong ability to mine potential information in data. By improving the loss function of the neural network, this paper reduces the influence of unbalanced data on the classification results and improves the classification effect of the model on a small number of categories. Aiming at the different lengths of Internet text, a more robust model of text sentiment classification is proposed, which makes the HCB-Att model better extract the local information and contextual information of the text. Finally, through comparative experiments, the optimization model used in this paper is proved to be effective for the analysis of network public opinion sentiment.
在互联网交流为主导的语境下，人们的各种情绪都可以通过网络舆情清晰地反映出来，无论是对政治事务的看法、对娱乐的偏好，还是对生活的需求。这也使得管理者或提供者能够更有针对性地满足他们的需求。基于当今了解网络舆论趋势的需求，本文描述了一种深度神经网络（DNN）。深度神经网络是一种机器学习模型，是深度学习的基础，具有挖掘数据中潜在信息的强大能力。通过改进神经网络的损失函数，本文减少了不平衡数据对分类结果的影响，提高了模型对少数类别的分类效果。针对互联网文本长度不同的问题，提出了一种更稳健的文本情感分类模型，使 HCB-Att 模型能够更好地提取文本的局部信息和上下文信息。最后，通过对比实验，证明了本文中使用的优化模型对网络舆情情感分析是有效的。
