Prediction of age, sentiment, and connectivity from social media text

Nguyen, Thin, Phung, Dinh, Adams, Brett and Venkatesh, Svetha 2011, Prediction of age, sentiment, and connectivity from social media text, in WISE 2011 : Web Information Systems Engineering : 12th International Conference, Sydney, Australia, October 13-14 2011 : proceedings, Springer-Verlag, Berlin, Germany, pp. 227-240, doi: 10.1007/978-3-642-24434-6_17.

Attached Files
Name Description MIMEType Size Downloads

Title Prediction of age, sentiment, and connectivity from social media text
Author(s) Nguyen, ThinORCID iD for Nguyen, Thin orcid.org/0000-0003-3467-8963
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Adams, Brett
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Web Information System Engineering. Conference (12th : 2011 : Sydney, New South Wales)
Conference location Sydney, New South Wales
Conference dates 13-14 Oct. 2011
Title of proceedings WISE 2011 : Web Information Systems Engineering : 12th International Conference, Sydney, Australia, October 13-14 2011 : proceedings
Editor(s) Bouguettaya, Athman
Hauswirth, Manfred
Liu, Ling
Publication date 2011
Series Lecture notes in computer science ; 6997
Conference series Web Information System Engineering Conference
Start page 227
End page 240
Total pages 14
Publisher Springer-Verlag
Place of publication Berlin, Germany
Keyword(s) binary classifiers
bloggers
classification tasks
discriminative features
linguistic analysis
linguistic features
social media
Summary Social media corpora, including the textual output of blogs, forums, and messaging applications, provide fertile ground for linguistic analysis material diverse in topic and style, and at Web scale. We investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity. These properties are found to be significantly different across the examined cohorts, which suggest discriminative features for a number of useful classification tasks. We build binary classifiers for old versus young bloggers, social versus solo bloggers, and happy versus sad posts with high performance. Analysis of discriminative features shows that age turns upon choice of topic, whereas sentiment orientation is evidenced by linguistic style. Good prediction is achieved for social connectivity using topic and linguistic features, leaving tagged mood a modest role in all classifications.
ISBN 3642244343
9783642244346
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-642-24434-6_17
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044670

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 7 times in TR Web of Science
Scopus Citation Count Cited 9 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 678 Abstract Views, 8 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 13:24:23 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.