中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Visual-Textual Sentiment Analysis in Product Reviews
SIAT OpenIR  > 数字所
Visual-Textual Sentiment Analysis in Product Reviews
Jin Ye; Xiaojiang Peng; Yu Qiao; Hao Xingy; Junli Liy; Rongrong Jiz.
2019
Conference NameICIP, 2019
Conference Date2019
Conference Place台湾台北
AbstractSentiment analysis has attracted increasing attention recently due to its potential wide applications in opinion analysis, recommendation system, etc. Visual-textual sentiment analysis aims to improve the performance of sentiment analysis by leveraging both visual and textual signals. In this paper, we address the visual-textual sentiment analysis in product reviews. Our main contributions are two-fold. First, instead of crawling data from Flickr or Twitter with positive and negative labels in existing works, we introduce a new dataset for visual-textual sentiment analysis, termed as Product Reviews150K (PR-150K), which is collected from the product reviews of online shopping websites. Second, we propose a deep Tucker fusion method for visual-textual sentiment analysis, which efficiently combines visual and textual deep representations based on the Tucker decomposition and a bilinear pooling operation. Extensive experiments on our PR-150K, MVSO, and VSO datasets show that our method outperforms several state-of-the-art methods.
Department多媒体
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/15907
Collection数字所
Recommended Citation
GB/T 7714
Jin Ye,Xiaojiang Peng,Yu Qiao,et al. Visual-Textual Sentiment Analysis in Product Reviews[C],2019.
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