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Algorithms for the thematic analysis of twitter datasets

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aneeshabakharia

Finding themes in tweets using Non-negative Matrix and Tensor Factorization

FormationTechnologie
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Algorithms for the Thematic Analysis of Twitter Datasets Twitter: aneesha Email: aneesha.bakharia@gmail.com #comtech2011 Twitter Workshop Presented by: Aneesha Bakharia
Background ,[object Object],[object Object],[object Object],Surveys Workshops Interviews Large Doc Collections Corpus Twitter Blog Comments
Types of Qualitative Content Analysis (Hsieh and Shannon, 2006) Concentrate on Summative and Conventional (Inductive) Coding Approach Study Begins With Derivation of Codes Algorithms Summative Keywords Keywords identified before and during analysis Unsupervised and semi-supervised algorithms: NMF , NTF LDA and traditional clustering algorithms. Conventional (Inductive) Observation Categories developed during analysis Directed (Deductive) Theory Categories derived from pre-existing theory prior to analysis Supervised classification algorithms: Support Vector Machines
Algorithms for Summative and Conventional Content Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Research ,[object Object],[object Object],[object Object]
Non-negative Matrix Factorisation ,[object Object],[object Object],[object Object],[object Object],Term-Tweet Matrix Specify No Themes (k) Features Matrix Weights Matrix Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Word 1 Word 2 Word n Theme 1 0.5 0 1 Theme 2 0 0.5 0 Theme 1 Theme 2 Tweet 1 1 0 Tweet 2 0 1 Tweet 3 0 1
Non-negative Matrix Factorisation Features Matrix Weights Matrix Theme 1 Theme 2 Word 1 Word 2 Word 2 Tweet 1 Tweet 1 Tweet 1 Word 1 Word 2 Word 3 Theme 1 0.5 0 1 Theme 2 0 0.5 0 Theme 1 Theme 2 Tweet 1 1 0 Tweet 2 0 1 Tweet 3 0 1
Applying NMF and LDA as Content Analysis aids
Non-negative Matrix Factorisation Tweet - Word Matrix Tweet – Author Matrix Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Word 1 Word 2 Word n Tweet Author 1 1 0 2 Tweet Author 2 0 1 0 Tweet Author 3 0 1 1
Algorithms for the Thematic Analysis of Tweets ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
#OzChi Analysis – OzChi 2010 Conference ,[object Object],[object Object],[object Object],[object Object]
TreeCloud Analysis of #OzChi Create Treeclouds: http://www.lirmm.fr/~gambette/treecloud/
OzChi Abstracts (2006 – 2010) http://www.randomsyntax.com/2010/11/24/uncovering-research-themes-from-5-years-of-ozchi-conferences-2006-2010/
Non-negative Tensor Matrix Factorisation Tweet – Word - Time Matrix Month April Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 March Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Feb Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Jan Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1
Non-negative Tensor Matrix Factorisation Nonnegative Tensor Factorization for Knowledge Discovery http://cisml.utk.edu/Seminars/2010/Berry.pdf CISML Seminar Series, Fall 2010, Michael W. Berry
[object Object],Algorithms for the Thematic Analysis of Tweets
[object Object],[object Object],[object Object],Algorithms for the Thematic Analysis of Tweets
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Toolkit
Looking for Collaborators Twitter: aneesha Email: aneesha.bakharia@gmail.com Twitter Graphics from Webdesigner Depot http:// www.webdesignerdepot.com Graphics converted to wmf format by Elizabeth Hall

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Algorithms for the thematic analysis of twitter datasets

  • 1. Algorithms for the Thematic Analysis of Twitter Datasets Twitter: aneesha Email: aneesha.bakharia@gmail.com #comtech2011 Twitter Workshop Presented by: Aneesha Bakharia
  • 2.
  • 3. Types of Qualitative Content Analysis (Hsieh and Shannon, 2006) Concentrate on Summative and Conventional (Inductive) Coding Approach Study Begins With Derivation of Codes Algorithms Summative Keywords Keywords identified before and during analysis Unsupervised and semi-supervised algorithms: NMF , NTF LDA and traditional clustering algorithms. Conventional (Inductive) Observation Categories developed during analysis Directed (Deductive) Theory Categories derived from pre-existing theory prior to analysis Supervised classification algorithms: Support Vector Machines
  • 4.
  • 5.
  • 6.
  • 7. Non-negative Matrix Factorisation Features Matrix Weights Matrix Theme 1 Theme 2 Word 1 Word 2 Word 2 Tweet 1 Tweet 1 Tweet 1 Word 1 Word 2 Word 3 Theme 1 0.5 0 1 Theme 2 0 0.5 0 Theme 1 Theme 2 Tweet 1 1 0 Tweet 2 0 1 Tweet 3 0 1
  • 8. Applying NMF and LDA as Content Analysis aids
  • 9. Non-negative Matrix Factorisation Tweet - Word Matrix Tweet – Author Matrix Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Word 1 Word 2 Word n Tweet Author 1 1 0 2 Tweet Author 2 0 1 0 Tweet Author 3 0 1 1
  • 10.
  • 11.
  • 12. TreeCloud Analysis of #OzChi Create Treeclouds: http://www.lirmm.fr/~gambette/treecloud/
  • 13. OzChi Abstracts (2006 – 2010) http://www.randomsyntax.com/2010/11/24/uncovering-research-themes-from-5-years-of-ozchi-conferences-2006-2010/
  • 14. Non-negative Tensor Matrix Factorisation Tweet – Word - Time Matrix Month April Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 March Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Feb Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1 Jan Word 1 Word 2 Word n Tweet 1 1 0 2 Tweet 2 0 1 0 Tweet 3 0 1 1
  • 15. Non-negative Tensor Matrix Factorisation Nonnegative Tensor Factorization for Knowledge Discovery http://cisml.utk.edu/Seminars/2010/Berry.pdf CISML Seminar Series, Fall 2010, Michael W. Berry
  • 16.
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  • 18.
  • 19. Looking for Collaborators Twitter: aneesha Email: aneesha.bakharia@gmail.com Twitter Graphics from Webdesigner Depot http:// www.webdesignerdepot.com Graphics converted to wmf format by Elizabeth Hall