The Lifecycle Change Model based on Wordle analysis and study
DOI:
https://doi.org/10.54097/hset.v66i.11671Keywords:
Lifecycle Change Model; GLoVe-LBP Neural Network; K-means.Abstract
Wordle is a popular game, and I did a detailed analysis of the Wordle dataset provided by the New York Times to better understand the game. First, we proposed a lifetime change model to describe the number of reported results over time, and through analysis we found that the property of "whether a word contains repeated letters" affects the fraction percentage of the difficult pattern. We then constructed a GloVe-LBP neural network to predict the reported percentage of a given solution and performed a detailed analysis of the model confidence, loss function, training adequacy, and generalization ability. Next, we constructed a K-Means and z-score classification model to classify the difficulty of words, through which the "EERIE" difficulty level was level 5. The relationship between the number of reported results and the number of difficult patterns in the data set was concluded and analyzed.
Downloads
References
LIU H, LI S, HUANG X. Adaptive surrogate model coupled with stochastic configuration network strategies for time-dependent reliability assessment [J]. Probabilistic Engineering Mechanics, 2023, 71.
SOLAK S, ALTINISIK U. A new method for classifying nuts using image processing and k-means plus plus clustering [J]. Journal of Food Process Engineering, 2018, 41(7).
WEI X, SHANG J. Mobile value chain collaboration for product diffusion: Role of the lifecycle [J]. Expert Systems with Applications, 2023, 215.
YU S-S, CHU S-W, WANG C-M, et al. Two improved k-means algorithms [J]. Applied Soft Computing, 2018, 68: 747-55.
Kelly, B. F., & Laws, K. R. (2022). Wordle: A game to improve word retrieval in aphasia. Aphasiology, 36(1), 25-41. doi: 10.1080/02687038.2021.1926095
Linsley, D., & Waring, R. (2021). Investigating the effectiveness of Wordle as a vocabulary teaching tool for EFL learners. Innovation in Language Learning and Teaching, 15(3), 288-300. doi: 10.1080/17501229.2019.1614439
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100–108. https://doi.org/10.2307/2346830
Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
