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Design of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM

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Abstract

Sentiment analysis techniques used on texts play an important role in many fields including decision making systems. A variety of research has been actively conducted on sentiment analysis techniques such as an approach using word frequency or morphological analysis, and the method of using a complex neural network. In this paper, we apply sentiment analysis technology using a deep neural network to sightseeing reviews, add ratings to reviews which had not included them, supplement data to enable various classification by weather or season, and design a system that enables custom recommendations based on data. Finally, we examine the contextual features of tourist attractions and design an efficient pre-processing procedure based on the results, and describe the overall process such as building a suitable learning environment, combining review and weather information, and final recommendation method.

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Acknowledgements

This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2017R1A2B4008886).

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Authors and Affiliations

  1. Department of Computer Engineering, Hoseo University, Asan, Republic of Korea

    Hyeon-woo An & Nammee Moon

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  1. Hyeon-woo An
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  2. Nammee Moon
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Correspondence to Nammee Moon.

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An, Hw., Moon, N. Design of recommendation system for tourist spot using sentiment analysis based on CNN-LSTM. J Ambient Intell Human Comput 13, 1653–1663 (2022). https://doi.org/10.1007/s12652-019-01521-w

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  • DOI: https://doi.org/10.1007/s12652-019-01521-w

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