Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

6-2011

Abstract

Summarizing and analyzing Twitter content is an important and challenging task. In this paper, we propose to extract topical keyphrases as one way to summarize Twitter. We propose a context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking. We evaluate our proposed methods on a large Twitter data set. Experiments show that these methods are very effective for topical keyphrase extraction.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Management and Analytics

Publication

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Portland, Oregon, June 19-24, 2011

First Page

379

Last Page

388

Publisher

ACL

City or Country

Stroudsburg, PA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://www.aclweb.org/anthology/P11-1039

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