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Content Analysis Through the Machine Learning Mill

Author

Listed:
  • Vivi Nastase

    (University of Ottawa)

  • Sabine Koeszegi

    (University of Vienna)

  • Stan Szpakowicz

    (University of Ottawa
    Institute of Computer Science)

Abstract

We present an analysis of partial automation of content analysis using machine learning methods. We use a decision-tree induction system to learn from manually categorized negotiation transcripts of electronic buyer–seller negotiations. The data we use were gathered using the Web-based negotiation support systems Inspire and SimpleNS. We experiment with various ways of representing the data to find the solution that gives the best results. The experiments show that we can identify, in relatively small data sets, linguistic features of interest for the detection of negotiation behaviour and negotiation-specific topics.

Suggested Citation

  • Vivi Nastase & Sabine Koeszegi & Stan Szpakowicz, 2007. "Content Analysis Through the Machine Learning Mill," Group Decision and Negotiation, Springer, vol. 16(4), pages 335-346, July.
    • Handle: RePEc:spr:grdene:v:16:y:2007:i:4:d:10.1007_s10726-006-9053-7
    DOI: 10.1007/s10726-006-9053-7
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    References listed on IDEAS

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    1. Lee Sproull & Sara Kiesler, 1986. "Reducing Social Context Cues: Electronic Mail in Organizational Communication," Management Science, INFORMS, vol. 32(11), pages 1492-1512, November.
    2. John L. Graham & Alma T. Mintu & Waymond Rodgers, 1994. "Explorations of Negotiation Behaviors in Ten Foreign Cultures Using a Model Developed in the United States," Management Science, INFORMS, vol. 40(1), pages 72-95, January.
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    Keywords

    content analysis; machine learning;

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