{"type":"ScholarlyArticle","title":"Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media","authors":[{"name":"Lizi Liao","slug":"lizi-liao","given":"Lizi","family":"Liao","affiliation":"School of Computing , National University of Singapore"},{"name":"Xiangnan He","slug":"xiangnan-he","given":"Xiangnan","family":"He","affiliation":"School of Computing , National University of Singapore"},{"name":"Zhaochun Ren","slug":"zhaochun-ren","given":"Zhaochun","family":"Ren","affiliation":"Data Science Lab , JD.com"},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Huan Xu","slug":"huan-xu","given":"Huan","family":"Xu","affiliation":"ISyE , Georgia Institute of Technology"},{"name":"Tat-Seng Chua","slug":"tat-seng-chua","given":"Tat-Seng","family":"Chua","affiliation":"School of Computing , National University of Singapore"}],"abstract":"Owing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring their brands’ reputation and the opinions of general public. To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media. Previous efforts have treated it as a traditional information extraction task, and forgo the specific properties of social media, such as the possible noise in user generated posts and the varying impacts; In contrast, we extract aspects by maximizing their representativeness, which is a new notion defined by us that accounts for both the coverage of aspects and the impact of posts. We formalize it as a submodular optimization problem, and develop a FastPAS algorithm to jointly select representative posts and aspects. The FastPAS algorithm optimizes parameters in a greedy way, which is highly efficient and can reach a good solution with theoretical guarantees. We perform extensive experiments on two datasets, showing that our method outperforms the state-of-the-art aspect extraction and summarization methods in identifying representative aspects.","doi":"10.24963/ijcai.2017/44","url":"https://ijcai.org/proceedings/2017/44","pdf":"https://ijcai.org/proceedings/2017/0044.pdf","markdown":"https://ijcai.org/proceedings/2017/44.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/44","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Agent-based and Multi-agent Systems","track":"Main track","number":44,"pages":{"first":310,"last":316},"keywords":["Natural Language Processing: Information Extraction","Agent-based and Multi-agent Systems: Economic paradigms, auctions and market-based systems"],"videos":[],"references":[{"position":1,"raw":"[Carbonell and Goldstein, 1998] Jaime Carbonell and Jade Gold- stein. 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