Please use this identifier to cite or link to this item: https://doi.org/10.24963/ijcai.2017/44
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dc.titleRepresentativeness-aware Aspect Analysis for Brand Monitoring in Social Media
dc.contributor.authorLizi Liao
dc.contributor.authorXiangnan He
dc.contributor.authorZhaochun Ren
dc.contributor.authorLiqiang Nie
dc.contributor.authorHuan Liu
dc.contributor.authorTat-Seng Chua
dc.date.accessioned2020-04-30T00:15:02Z
dc.date.available2020-04-30T00:15:02Z
dc.date.issued2017-08-19
dc.identifier.citationLizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Liu, Tat-Seng Chua (2017-08-19). Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media. IJCAI 2017 : 310-316. ScholarBank@NUS Repository. https://doi.org/10.24963/ijcai.2017/44
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/167449
dc.description.abstractOwing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring the reputation of their brands 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 treat it as a traditional information extraction task, and forgo the speci?c 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 de?ned 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 ef?cient and can reach a good solution with theoretical guarantees. Extensive experiments on two datasets demonstrate that our method outperforms state-of-the-art aspect extraction and summarization methods in identifying representative aspects.
dc.typeConference Paper
dc.contributor.departmentDEPT OF COMPUTER SCIENCE
dc.description.doi10.24963/ijcai.2017/44
dc.description.sourcetitleIJCAI 2017
dc.description.page310-316
dc.grant.idR-252-300-002-490
dc.grant.fundingagencyInfocomm Media Development Authority
dc.grant.fundingagencyNational Research Foundation
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