Location oriented phrase detection in microblogs

Hosseini, Saeid, Unankard, Sayan, Zhou, Xiaofang and Sadiq, Shazia (2014). Location oriented phrase detection in microblogs. In: Sourav S. Bhowmick, Curtis E. Dyreson, Christian S. Jensen, Mong Li Lee, Agus Muliantara and Bernhard Thalheim, Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings. 19th International Conference on Database Systems for Advanced Applications (DASFAA 2014), Bali, Indonesia, (495-509). 21- 24 April 2014. doi:10.1007/978-3-319-05810-8_33

Author Hosseini, Saeid
Unankard, Sayan
Zhou, Xiaofang
Sadiq, Shazia
Title of paper Location oriented phrase detection in microblogs
Conference name 19th International Conference on Database Systems for Advanced Applications (DASFAA 2014)
Conference location Bali, Indonesia
Conference dates 21- 24 April 2014
Proceedings title Database Systems for Advanced Applications - 19th International Conference, DASFAA 2014, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Heidelberg, Germany
Publisher Springer Verlag
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.1007/978-3-319-05810-8_33
Open Access Status
ISBN 9783319058092
ISSN 0302-9743
Editor Sourav S. Bhowmick
Curtis E. Dyreson
Christian S. Jensen
Mong Li Lee
Agus Muliantara
Bernhard Thalheim
Volume 8421
Issue PART 1
Start page 495
End page 509
Total pages 15
Chapter number 33
Total chapters 33
Collection year 2015
Language eng
Abstract/Summary As a successful micro-blogging service, Twitter has demonstrated unprecedented popularity and international reach. Location extraction from micro-blogs (tweets) on this domain is an important challenge and can harness noisy but rich contents. Extracting location information can enable a variety of applications such as query-by-location, local advertising, crises awareness and also systems designed to provide information about events, points of interests (POIs) and landmarks. Considering the high throughput rate in Twitter space, we propose an approach to detect location-oriented phrases solely relying on tweet contents. The system finds associated phrases dedicated to each specific scalable geographical area. We have evaluated our approach based on real-world Twitter dataset from Australia. We conducted a comprehensive comparison between strong local terms (uni-word) and phrases (multi-words). Our experiments verify the system's capabilities using multiple trending baselines and demonstrate that our phrase based approach can better specify locality instead of words.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword Location oriented phrases
Spatial data mining
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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