MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis | |
Xu, Nan1,2; Mao, Wenji1,2 | |
2017-11 | |
会议日期 | November 6-10, 2017 |
会议地点 | Singapore |
英文摘要 | With the prevalence of more diverse and multiform user-generated content in social networking sites, multimodal sentiment analysis has become an increasingly important research topic in recent years. Previous work on multimodal sentiment analysis directly extracts feature representation of each modality and fuse these features for classification. Consequently, some detailed semantic information for sentiment analysis and the correlation between image and text have been ignored. In this paper, we propose a deep semantic network, namely MultiSentiNet, for multimodal sentiment analysis. We first identify object and scene as salient detectors to extract deep semantic features of images. We then propose a visual feature guided attention LSTM model to extract words that are important to understand the sentiment of whole tweet and aggregate the representation of those informative words with visual semantic features, object and scene. The experiments on two public available sentiment datasets verify the effectiveness of our MultiSentiNet model and show that our extracted semantic features demonstrate high correlations with human sentiments. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39143] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
通讯作者 | Xu, Nan |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Xu, Nan,Mao, Wenji. MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis[C]. 见:. Singapore. November 6-10, 2017. |
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