{"type":"ScholarlyArticle","title":"Unsupervised Sentiment Analysis for Social Media Images","authors":[{"name":"Yilin Wang","slug":"yilin-wang","given":"Yilin","family":"Wang","affiliation":""},{"name":"Suhang Wang","slug":"suhang-wang","given":"Suhang","family":"Wang","affiliation":""},{"name":"Jiliang Tang","slug":"jiliang-tang","given":"Jiliang","family":"Tang","affiliation":""},{"name":"Huan Liu","slug":"huan-liu","given":"Huan","family":"Liu","affiliation":""},{"name":"Baoxin Li","slug":"baoxin-li","given":"Baoxin","family":"Li","affiliation":""}],"abstract":"Recently text-based sentiment prediction has been extensively studied, while image-centric sentiment analysis receives much less attention. In this paper,we study the problem of understanding human sentiments from large-scale social media images,considering both visual content and contextual information,such as comments on the images, captions,etc. The challenge of this problem lies in the “semantic gap” between low-level visual features and higher-level image sentiments. Moreover, the lack of proper annotations/labels in the majority of social media images presents another challenge.To address these two challenges, we propose a novel Unsupervised SEntiment Analysis (USEA) framework for social media images. Our approach exploits relations among visual content and relevant contextual information to bridge the “semantic gap” in the prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.","doi":null,"url":"https://ijcai.org/proceedings/2015/336","pdf":"https://ijcai.org/proceedings/2015/0336.pdf","markdown":"https://ijcai.org/proceedings/2015/336.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/336","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web Mining","track":"Main Track","number":336,"pages":{"first":2378,"last":2378},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}