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The StaViCTA Group Report for RepLab 2014: Reputation Dimensions Task
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)
Gavagai AB.
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
Lund University.ORCID iD: 0000-0002-7240-9003
2014 (English)In: Working Notes for CLEF 2014 Conference: Sheffield, UK, September 15-18, 2014 / [ed] Linda Cappellato, Nicola Ferro, Martin Halvey, Wessel Kraaij, CEUR-WS.org , 2014, 1519-1527 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present our experiments on the RepLab 2014 Reputation Dimension task. RepLab is a competitive challenge for Reputation Management Systems. RepLab 2014’s reputation dimensions task focuses on categorization of Twitter messages with regard to standard reputation dimensions (such as performance, leadership, or innovation). Our approach only relies on the textual content of tweets and ignores both metadata and the content of URLs within tweets. We carried out several experiments focusing on different feature sets including bag of n-grams, distributional semantics features, and deep neural network representations. The results show that bag of bigram features with minimum frequency thresholding work quite well in reputation dimension task especially with regards to average F1 measure over all dimensions where two of our four submitted runs achieve highest and second highest scores. Our experiments also show that semi-supervised recursive autoencoders outperform other feature sets used in our experiments with regards to accuracy measure and is a promising subject of future research for improvements. 

Place, publisher, year, edition, pages
CEUR-WS.org , 2014. 1519-1527 p.
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 1180
Keyword [en]
short text categorization, sentiment analysis, reputation monitoring
National Category
Computer Science Language Technology (Computational Linguistics)
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-36012Scopus ID: 2-s2.0-84961309323OAI: oai:DiVA.org:lnu-36012DiVA: diva2:733406
Conference
CLEF 2014 Evaluation Labs and Workshop
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Available from: 2014-07-09 Created: 2014-07-09 Last updated: 2016-12-16Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf