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Feature Design for Polarity Classification Presentation for FEAST (Saarland University) 22nd April 2009 By Michael Wiegand Spoken Language Systems (IRTG) Outline of Talk Introduction to Polarity Classification Semi-Supervised


  1. Feature Design for Polarity Classification Presentation for FEAST (Saarland University) 22nd April 2009 By Michael Wiegand Spoken Language Systems (IRTG)

  2. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • The Task • Related Work • Feature Design • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 2

  3. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • The Task • Related Work • Feature Design • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 3

  4. What is Polarity Classification? • Polarity Classification is a subtask in Opinion Mining • 2 different types of text classification in Opinion Mining: • Subjectivity Detection • Does a text represent an ������� or a ���� ? • ������������������������������������������� vs. ���� ����������������������������������������������� ����������������������������������������������� • Polarity Classification • Given an opinionated text, is the opinion expressed in the text �������� or �������� ? • �������������� vs. ��������������� 4

  5. What is Polarity Classification? • Polarity Classification is a subtask in Opinion Mining • 2 different types of text classification in Opinion Mining: • Subjectivity Detection • Does a text represent an ������� or a ���� ? • ������������������������������������������� vs. ���� ����������������������������������������������� ����������������������������������������������� • Polarity Classification • Given an opinionated text, is the opinion expressed in the text ��������� or �������� ? • �������������� vs. ��������������� 5

  6. Why Polarity Classification? • Increasingly more opinionated content on the web (Web 2.0) � need for retrieving/classifying this kind of content • What makes polarity classification difficult? • Different from common topic classification • Different from common topic classification • Different kind of cues: ������������������ (e.g. ���� , �� �� etc.); not necessarily frequent content words! • ������������������ ( ��!����� , �����"����� ) • ����������������� of polar expressions (e.g. ����� � ��� vs. ����� ��������� ���� ) 6

  7. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • The Task • Related Work • Feature Design • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 7

  8. Semi-Supervised Learning - an Illustration 8

  9. Semi-Supervised Learning - an Illustration ������������ ������������ ���������� ���� 9

  10. Semi-Supervised Learning - an Illustration ������������ ������������ ���� 10

  11. Semi-Supervised Learning - an Illustration ���������������������� �������������� 11

  12. Semi-Supervised Learning - an Illustration �������������������� �������������������� ����������� ����������� ������� ��������������������� ���������������� 12

  13. Semi-Supervised Learning - an Illustration �������������������� �������������������� ����������� ����������� ������� ��������������������� ���������������� 13

  14. Semi-Supervised Learning - an Illustration �������������������� �������������������� ����������� ����������� ������� ��������������������� ���������������� 14

  15. Semi-Supervised Learning - an Illustration 15

  16. Semi-Supervised Learning - an Illustration ����������� ���������������� ������������� ������������� ���������������� ������� ��������� ���� 16

  17. Semi-Supervised Learning - an Illustration ������������ ������������ �������� ������� �������������� �������� 17

  18. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • �������� • Related Work • Feature Design • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 18

  19. The task • Document-level text classification of reviews • Decide whether a document is either a positive or a negative review • Use labeled and unlabeled documents for training training • All documents, both labeled and unlabeled, are assumed to be subjective ( ������������������� ��#$������% ) • All documents, both labeled and unlabeled, are either positive or negative reviews 19

  20. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • The Task • ������������ • Feature Design • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 20

  21. Related Work • Supervised Learning: • Different algorithms and feature selection/extraction methods [Pang 2002; Salvetti 2006; Ng 2006; Gamon 2004] • Unsupervised Learning: • Induction of polarity lexicons (i.e. identification of • Induction of polarity lexicons (i.e. identification of polar expression) using ����������&������ �� ��&������ [Turney 2002] • Semi-Supervised Learning: • Extending Turney‘s webmining approach with labeled data [Beineke 2004] • EM in the context of domain adaptation [Aue 2005] 21

  22. Contribution of this work • ����� extensive study of semi-supervised learning for polarity classification • Comparison of different feature sets • Evaluation on various domains • Evaluation on various domains 22

  23. Outline of Talk • Introduction to Polarity Classification • Semi-Supervised Learning for Document-Level Classification • The Task • Related Work • �������������� • Experiments • Supervised Learning Sentence-Level Classification • The Task • Related Work • Feature Design • Experiments • Topic-Related Sentence-Level Classification �������������� • Conclusion 23

  24. Why is feature selection more important in semi- supervised learning than in supervised learning? • Less information contained in small labeled datasets � intrinsic predictiveness of features is important • Inappropriate feature sets may lead • Inappropriate feature sets may lead semi-supervised classifiers astray • In polarity classification there is the danger that topic information interferes 24

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