Topical Keyphrase Extraction from Twitter

• Main Authors: ZHAO, Xin, JIANG, Jing, HE, Jing, SONG, Yang, ACHANANUPARP, Palakorn, LIM, Ee Peng, LI, Xiaoming
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/1363; https://ink.library.smu.edu.sg/context/sis_research/article/2362/viewcontent/ACL_11.pdf
• Institution: Singapore Management University

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.