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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 the reputation of their brands and the opinions of the general public. To help companies in monitoring their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ freetext posts in social media. While previous efforts have treated it as a traditional information extraction task, they forgo the specific properties of social media, such as the possible noises in user generated posts and the varying impacts. In contrast to these approaches, we extract aspects by maximizing their representativeness, which is a new notion defined by us that accounts for both the coverage of aspects and impact of posts. We formalize it as a submodular optimization problem, developing 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. Extensive experiments on two datasets demonstrate that our method outperforms state-of-the-art aspect extraction and summarization methods in identifying representative aspects.

Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media