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Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

Author

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  • Young Bin Kim
  • Sang Hyeok Lee
  • Shin Jin Kang
  • Myung Jin Choi
  • Jung Lee
  • Chang Hun Kim

Abstract

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.

Suggested Citation

  • Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0132944
    DOI: 10.1371/journal.pone.0132944
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    References listed on IDEAS

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    Cited by:

    1. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    2. Alessandra Cretarola & Gianna Fig`a-Talamanca & Marco Patacca, 2017. "A sentiment-based model for the BitCoin: theory, estimation and option pricing," Papers 1709.08621, arXiv.org.
    3. Edson Pindza & Jules Clement Mba & Sutene Mwambi & Nneka Umeorah, 2023. "Neural Network for valuing Bitcoin options under jump-diffusion and market sentiment model," Papers 2310.09622, arXiv.org.
    4. Alessandra Cretarola & Gianna Fig`a-Talamanca, 2017. "A confidence-based model for asset and derivative prices in the BitCoin market," Papers 1702.00215, arXiv.org.
    5. Alessandra Cretarola & Gianna Figà-Talamanca & Marco Patacca, 2020. "Market attention and Bitcoin price modeling: theory, estimation and option pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(1), pages 187-228, June.
    6. Young Bin Kim & Kyeongpil Kang & Jaegul Choo & Shin Jin Kang & TaeHyeong Kim & JaeHo Im & Jong-Hyun Kim & Chang Hun Kim, 2017. "Predicting the Currency Market in Online Gaming via Lexicon-Based Analysis on Its Online Forum," Complexity, Hindawi, vol. 2017, pages 1-10, December.
    7. Alessandra Cretarola & Gianna Figà-Talamanca, 2021. "Detecting bubbles in Bitcoin price dynamics via market exuberance," Annals of Operations Research, Springer, vol. 299(1), pages 459-479, April.

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