Interest Mining from User Tweets

被引：2

作者：

Thuy Vu ^{[1
]}

Perez, Victor ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90095 USA

来源：

PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13) | 2013年

关键词：

keyword/keyphrase extraction; keyword/keyphrase ranking; topic modeling; data processing; social networks; Twitter;

D O I：

10.1145/2505515.2507883

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We build a system to extract user interests from Twitter messages. Specifically, we extract interest candidates using linguistic patterns and rank them using four different keyphrase ranking techniques: TFIDF, TextRank, LDA-TextRank, and Relevance-Interestingness-Rank (RI-Rank). We also explore the complementary relation between TFIDF and TextRank in ranking interest candidates. Top ranked interests are evaluated with user feedback gathered from an online survey. The results show that TFIDF and TextRank are both suitable for extracting user interests from tweets. Moreover, the combination of TFIDF and TextRank consistently yields the highest user positive feedback.

引用

页码：1869 / 1872

页数：4

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