School of Computer, Beijing Information Science and Technology University, Beijing 100101, China.
Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum-Beijing, Beijing 102249, China.
Comput Intell Neurosci. 2022 Aug 5;2022:2241310. doi: 10.1155/2022/2241310. eCollection 2022.
Dialogue sentiment analysis is a hot topic in the field of artificial intelligence in recent years, in which the construction of multimodal corpus is the key part of dialogue sentiment analysis. With the rapid development of the Internet of Things (IoT), it provides a new means to collect the multiparty dialogues to construct a multimodal corpus. The rapid development of Mobile Edge Computing (MEC) provides a new platform for the construction of multimodal corpus. In this paper, we construct a multimodal corpus on MEC servers to make full use of the storage space distributed at the edge of the network according to the procedure of constructing a multimodal corpus that we propose. At the same time, we build a deep learning model (sentiment analysis model) and use the constructed corpus to train the deep learning model for sentiment on MEC servers to make full use of the computing power distributed at the edge of the network. We carry out experiments based on real-world dataset collected by IoT devices, and the results validate the effectiveness of our sentiment analysis model.
对话情感分析是近年来人工智能领域的一个热门话题，其中多模态语料库的构建是对话情感分析的关键部分。随着物联网（IoT）的快速发展，它为收集多方对话以构建多模态语料库提供了新的手段。移动边缘计算（MEC）的快速发展为多模态语料库的构建提供了新的平台。在本文中，我们根据提出的多模态语料库构建过程，在 MEC 服务器上构建多模态语料库，充分利用分布在网络边缘的存储空间。同时，我们构建了一个深度学习模型（情感分析模型），并使用构建的语料库在 MEC 服务器上对深度学习模型进行情感训练，充分利用分布在网络边缘的计算能力。我们基于物联网设备收集的真实数据集进行实验，实验结果验证了我们的情感分析模型的有效性。
