DEVELOPMENT OF A K-NN MODEL TO PREDICT THE POLARITY OF KOREAN GAME REVIEW COMMENTS

Authors

  • Cho, Hyun-Woong Dept. of SW Analysis & Design, Seoul National University of Science and Technology, Seoul, Republic of Korea
  • Kim, Woo Je Dept. of SW Analysis & Design, Seoul National University of Science and Technology, Seoul, Republic of Korea

DOI:

https://doi.org/10.20319/mijst.2016.s11.108119

Keywords:

Opinion mining, K-NN, Polarity, Game Review Comment

Abstract

The purpose of this paper is to develop the machine learning model to evaluate game software in quantitative scale using opinion mining with the review comments which are provided by users in Korean. To do this, we first decompose the review comments into a lot of meaningful morphemes, and second construct a dictionary for opinion mining. Third, we develop a k-NN model to predict the polarity of review comment. Finally, we predict the polarity for each review comment which is included in validation data set by the model. The experimental results of the developed model are performed by the model which is implemented by JAVA and R language.

References

Kim, G., Lee, H., Yook, S., & Paik, W. (2009) Customer Preference Identification System using Natural Language Processing-based Analysis Korea Society for Information Management 16. 65-70.

Jang, K., Park, S., & Kim, W. (2015). Automatic construction of a negative/positive corpus and emotional classification using the internet emotional sign Journal of KIISE, 42(4). 512-521

Shin, J. & Kim, H. (2010) A Robust Pattern-based Feature Extraction Method for Sentiment Categorization of Korean Customer Reviews Journal of KIISE 37(12) 946-950

Song, J. & Lee, S. (2011). Automatic Construction of a Positive/Negative Feature-Predicate Dictionary for Polarity Classification of Product Reviews Journal of KIISE 38(3) 157-168.

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Published

2015-07-15

How to Cite

Hyun-Woong, C., & Woo Je, K. (2015). DEVELOPMENT OF A K-NN MODEL TO PREDICT THE POLARITY OF KOREAN GAME REVIEW COMMENTS. MATTER: International Journal of Science and Technology, 1(1), 108–119. https://doi.org/10.20319/mijst.2016.s11.108119

Issue

Vol. 1 No. 1 (2015): Special Issue

Section

Articles

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Copyright (c) 2015 Authors

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Creative Commons License
All published work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.