Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Data Management and Analytics
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Portland, Oregon, June 19-24, 2011
City or Country
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. Research Collection School Of Information Systems.
Available at: http://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 License.