Discovering context: Classifying tweets through a semantic transform based on wikipedia

Yegin Genc, Yasuaki Sakamoto, Jeffrey V. Nickerson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

55 Scopus citations

Abstract

By mapping messages into a large context, we can compute the distances between them, and then classify them. We test this conjecture on Twitter messages: Messages are mapped onto their most similar Wikipedia pages, and the distances between pages are used as a proxy for the distances between messages. This technique yields more accurate classification of a set of Twitter messages than alternative techniques using string edit distance and latent semantic analysis.

Original languageEnglish
Title of host publicationFoundations of Augmented Cognition
Subtitle of host publicationDirecting the Future of Adaptive Systems - 6th International Conference, FAC 2011, Held as Part of HCI International 2011, Proceedings
Pages484-492
Number of pages9
DOIs
StatePublished - 2011
Event6th International Conference on Foundations of Augmented Cognition, FAC 2011, Held as Part of HCI International 2011 - Orlando, FL, United States
Duration: 9 Jul 201114 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6780 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Foundations of Augmented Cognition, FAC 2011, Held as Part of HCI International 2011
Country/TerritoryUnited States
CityOrlando, FL
Period9/07/1114/07/11

Keywords

  • Text classification
  • Wikipedia
  • cognition
  • context
  • latent semantic analysis
  • semantics

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