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A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions

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Abstract

In this paper, we present a time series structure analysis method of a meeting using text data and a method for the visualization of state transitions. Our method evaluates and visualizes the convergence/divergence of the meeting in a time series using text data from the meeting. It is important to facilitate and review meetings for improving efficiency. Therefore, it is important not only to review the final agreement and conclusion in the dialogue during the meeting but also to understand the dialogue process. We introduce two indicators: freshness and representativeness. Our system expresses the status of the meeting in four quadrants (“stagnation”, “exploration”, “deepening”, and “consensus building”) corresponding to the combination of degrees of freshness and representativeness. By conducting an analysis using these indicators, we can objectively find which parts of the dialogue stagnated or advanced the discussion. In addition, it is possible to clarify the meeting process as a structure for review and facilitation, thereby improving efficiency of the meeting. Thus, we implemented a system that realizes this method. Furthermore, we applied this system to real data gathered from meetings consisting of actual multi-company members and verified its effectiveness.

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Acknowledgements

We wish to thank Kunihiko Ueshima (Japan Data Exchange Inc.) for his cooperation with the questionnaire.

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Authors and Affiliations

  1. Center for Global Communications (GLOCOM), International University of Japan, Tokyo, Japan

    Ryotaro Okada & Takafumi Nakanishi

  2. Faculty of Data Science, Asia AI Institute, Musashino University, Tokyo, Japan

    Takafumi Nakanishi

  3. ITOKI Corporation, Tokyo, Japan

    Yuichi Tanaka, Yutaka Ogasawara & Kazuhiro Ohashi

Authors
  1. Ryotaro Okada
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  2. Takafumi Nakanishi
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  3. Yuichi Tanaka
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  4. Yutaka Ogasawara
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  5. Kazuhiro Ohashi
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Corresponding author

Correspondence to Ryotaro Okada.

Additional information

In association with The Japanese Society for Artificial Intelligence.

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Okada, R., Nakanishi, T., Tanaka, Y. et al. A Time Series Structure Analysis Method of a Meeting Using Text Data and a Visualization Method of State Transitions. New Gener. Comput. 37, 113–137 (2019). https://doi.org/10.1007/s00354-018-0050-6

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  • DOI: https://doi.org/10.1007/s00354-018-0050-6

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