Representativeness-aware aspect analysis for brand monitoring in social media
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 a...
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sg-smu-ink.sis_research-85782022-12-12T08:11:00Z Representativeness-aware aspect analysis for brand monitoring in social media LIAO, Lizi HE, Xiangnan REN, Zhaochun NIE, Liqiang XU, Huan CHUA, Ta-Seng 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. 2017-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7575 info:doi/10.24963/ijcai.2017/44 https://ink.library.smu.edu.sg/context/sis_research/article/8578/viewcontent/Representativeness_aware_aspect_analysis_for_brand_monitoring_in_social_media.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Agent-based and multi-agent systems: economic paradigms auctions and market-based systems Natural language processing: information extraction Databases and Information Systems |
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Agent-based and multi-agent systems: economic paradigms auctions and market-based systems Natural language processing: information extraction Databases and Information Systems LIAO, Lizi HE, Xiangnan REN, Zhaochun NIE, Liqiang XU, Huan CHUA, Ta-Seng Representativeness-aware aspect analysis for brand monitoring in social media |
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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. |
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LIAO, Lizi HE, Xiangnan REN, Zhaochun NIE, Liqiang XU, Huan CHUA, Ta-Seng |
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LIAO, Lizi HE, Xiangnan REN, Zhaochun NIE, Liqiang XU, Huan CHUA, Ta-Seng |
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LIAO, Lizi |
title |
Representativeness-aware aspect analysis for brand monitoring in social media |
title_short |
Representativeness-aware aspect analysis for brand monitoring in social media |
title_full |
Representativeness-aware aspect analysis for brand monitoring in social media |
title_fullStr |
Representativeness-aware aspect analysis for brand monitoring in social media |
title_full_unstemmed |
Representativeness-aware aspect analysis for brand monitoring in social media |
title_sort |
representativeness-aware aspect analysis for brand monitoring in social media |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/7575 https://ink.library.smu.edu.sg/context/sis_research/article/8578/viewcontent/Representativeness_aware_aspect_analysis_for_brand_monitoring_in_social_media.pdf |
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