Topical Keyphrase Extraction from Twitter

Author

Xin ZHAO, Peking University
Jing JIANG, Singapore Management UniversityFollow
Jing HE, Peking University
Yang SONG, Peking University
Palakorn ACHANANUPARP, Singapore Management UniversityFollow
Ee Peng LIM, Singapore Management UniversityFollow
Xiaoming LI, Peking University

Publication Type

Conference Proceeding Article

Version

acceptedVersion

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

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

Citation

ZHAO, Xin; JIANG, Jing; HE, Jing; SONG, Yang; ACHANANUPARP, Palakorn; LIM, Ee Peng; and LI, Xiaoming. Topical Keyphrase Extraction from Twitter. (2011). Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Portland, Oregon, June 19-24, 2011. 379-388.
Available at: https://ink.library.smu.edu.sg/sis_research/1363

Creative Commons License

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

Additional URL

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