Analyzing Social Robotics Research with Natural Language Processing Techniques


The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different fields. This divergent expansion calls for a clear analysis of the field aimed at better orienting the research, thus paving the future of social robotics. This paper aims at understanding how the SR research field evolved in the last two decades by analyzing academic publications in SR and human–robot interaction using natural language processing (NLP) techniques. The analysis spotted an overlap between SR and human–robot interaction research fields that have been disambiguated using a data-driven approach that leads to the identification of a new group of papers we clustered under the concept of “soft HRI.” This research topic has been analyzed by extracting trends and insights. Finally, another topic modelling step has been applied to identify seven sub-topics that have been discussed and analyzed picturing the current state of the art of SR. The paper reports a complete overview of the SR research field identifying various topics and sub-topics helping researchers in understanding the evolution of this field, thus supporting the strategic placing and evolution of their research activities.

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Correspondence to Daniele Mazzei.

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Mazzei, D., Chiarello, F. & Fantoni, G. Analyzing Social Robotics Research with Natural Language Processing Techniques. Cogn Comput 13, 308–321 (2021).

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  • Social robotics
  • Human–robot interaction
  • Bibliometric analysis
  • Topic modelling
  • Natural language processing