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Combining TF-IDF and LDA to generate flexible communication for recommendation services by a humanoid robot

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Abstract

Linguistic flexibility around non-predetermined expressions is important for more effective human–robot face-to-face interaction. In the past, most robots have been fitted with a limited and supervised response process and programmed with certain responses for predetermined words or sentences. As a result, implementing viable robot-based recommendation services has been difficult. The purpose of this paper is to propose a text-mining approach to flexible robot-based recommendation services in which, when the robot encounters linguistic expressions that differ substantially from the programmed linguistic database, flexible responses are generated based on understanding of several external corpora and a knowledge-learning process. This study combines two text-mining methods, TF-IDF and LDA, to generate flexible communication, which enables a robot to respond with recommendation content that is not pre-programmed. The results of our analysis suggest that the proposed combined approach outperforms the TF-IDF and LDA methods in terms of overall accuracy and F-score.

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Acknowledgements

This work was funded by the National Strategic R&D Program for Industrial Technology (10041659) and funded by the Ministry of Trade, Industry, and Energy (MOTIE).

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Authors and Affiliations

  1. Department of IT Business Administration, Hanshin University, Osan, Gyeonggi, Republic of Korea

    Namyeon Lee

  2. CAITECH, Kyung Hee University, Seoul, Republic of Korea

    Eunji Kim

  3. School of Management, Kyung Hee University, Seoul, Republic of Korea

    Ohbyung Kwon

Authors
  1. Namyeon Lee
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  2. Eunji Kim
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  3. Ohbyung Kwon
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Correspondence to Ohbyung Kwon.

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Lee, N., Kim, E. & Kwon, O. Combining TF-IDF and LDA to generate flexible communication for recommendation services by a humanoid robot. Multimed Tools Appl 77, 5043–5058 (2018). https://doi.org/10.1007/s11042-017-5113-z

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