| 在线评论的静态多模态情感分析 |
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| 引用本文： | 王开心,徐秀娟,刘宇,赵哲焕,赵小薇. 在线评论的静态多模态情感分析[J]. 应用科学学报, 2022, 40(1): 25-35. DOI: 10.3969/j.issn.0255-8297.2022.01.003 |
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| 作者姓名： | 王开心 徐秀娟 刘宇 赵哲焕 赵小薇 |
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| 作者单位： | 1. 大连理工大学 软件学院, 辽宁 大连 116620;2. 大连理工大学 辽宁省泛在网络与服务软件重点实验室, 辽宁 大连 116620 |
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| 基金项目： | 国家自然科学基金（No.61672128）资助； |
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| 摘 要： | 提出了一种基于Pre-LN Transformer的静态多模态情感分类模型.该模型首先利用Pre-LN Transformer结构中的编码器提取评论文本中的语义特征,其中编码器的多头自注意力机制允许模型在不同的子空间内学到相关情感信息.然后根据ResNet提取评论的图像特征,在特征水平融合的基础上通过视觉方面注意力机制...
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| 关 键 词： | 情感分析 静态多模态 在线评论 视觉方面注意力 |
| 收稿时间： | 2021-07-25 |
Static Multimodal Sentiment Analysis of Online Reviews |
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| WANG Kaixin,XU Xiujuan,LIU Yu,ZHAO Zhehuan,ZHAO Xiaowei. Static Multimodal Sentiment Analysis of Online Reviews[J]. Journal of Applied Sciences, 2022, 40(1): 25-35. DOI: 10.3969/j.issn.0255-8297.2022.01.003 |
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| Authors: | WANG Kaixin XU Xiujuan LIU Yu ZHAO Zhehuan ZHAO Xiaowei |
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| Affiliation: | 1. School of Software Technology, Dalian University of Technology, Dalian 116620, Liaoning, China;2. Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian 116620, Liaoning, China |
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| Abstract: | This paper proposes a static multi-modal sentiment classification model based on Pre-LN Transformer. This model firstly extracts semantic features from reviews using the encoder in Pre-LN Transformer structure, in which the multi-head self-attention mechanism allows the model to learn relevant emotional information in different subspaces. Then our model extracts the image features according to ResNet in the reviews. On the basis of feature level fusion, the visual attention mechanism guides the sentiment classification of text and realizes the static multimodal sentiment analysis of online reviews. Experimental results show that our model improves the performance by 1.34% and 1.10% in evaluation accuracy than BiGRU-mVGG and Trans-mVGG on Yelp datasets, which verifies the effectiveness and feasibility of the proposed model. |
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| Keywords: | sentiment analysis static multimodal online reviews visual aspect attention |
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