Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2017

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.

Keywords

Agent-based and multi-agent systems: economic paradigms, auctions and market-based systems, Natural language processing: information extraction

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management; Intelligent Systems and Optimization

Publication

Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017 August 19 - 25

First Page

310

Last Page

316

ISBN

9780999241103

Identifier

10.24963/ijcai.2017/44

City or Country

Melbourne, Australia

Additional URL

https://doi.org/10.24963/ijcai.2017/44

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