Abstract
This paper introduces a system designed for automatically generating personalized annotation tags to label Twitter user's interests and concerns. We applied TFIDF ranking and TextRank to extract keywords from Twitter messages to tag the user. The user tagging precision we obtained is comparable to the precision of keyword extraction from web pages for content-targeted advertising. © 2010 Association for Computational Linguistics.
Cite
CITATION STYLE
Wu, W., Zhang, B., & Ostendorf, M. (2010). Automatic generation of personalized annotation tags for Twitter users. In NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference (pp. 689–692).
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