“Future Compass”, a Tool That Allows Us to See the Right Horizon—Integration of Topic Modeling and Multiple-Factor Analysis

Hiroaki Sugino (), Tatsuya Sekiguchi, Yuuki Terada and Naoki Hayashi
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Hiroaki Sugino: Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan
Tatsuya Sekiguchi: Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, Kyoto 606-0823, Japan
Yuuki Terada: Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan
Naoki Hayashi: Institute of Human and Social Sciences, Kanazawa University, Ishikawa 920-1192, Japan

Sustainability, 2023, vol. 15, issue 13, 1-20

Abstract: Coastal social–ecological systems (SES), particularly in large bays, are critical for fisheries, transportation, and disaster prevention in island and coastal countries. To achieve the sustainability of such bays, public involvement is recently considered inevitable for planning and management, but the increasing complexity of variables and future visions to be considered is one difficulty when trying to include many stakeholders and public opinions. To address this challenge, a free-associative description questionnaire survey was used in this study to extract holistic coastal residents’ future visions for Tokyo Bay, including both positive and negative outcomes. By integrating biterm topic modeling (BTM) and multiple-factor analysis (MFA), this study succeeded to aggregate and visualize the various future visions of Tokyo Bay with enhanced comprehensibility. As one outcome, the linkages and differences between the major topics in the positive and negative future visions were visualized as vectors in a correlation circle. Also, the study found that these two kinds of future vectors are not always polar opposites, but, rather, some of them are interlinked, pointing in the same direction. This highlights the importance of measuring the balance between two kinds of future vectors in consensus-building in order to search for the optimal future direction. Finally, the study discusses the potential of this method as a “Future Compass”, for implementing future-oriented consensus-building toward the sustainability of SES.

Keywords: consensus-building; future vision; natural language processing (NLP); biterm topic model (BTM); multiple-factor analysis (MFA); Tokyo Bay (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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