Wordle difficulty prediction model based on PSO-LSTM and hierarchical clusters

Xiaolu Sun; Yueyue Fan; Xingyan Cai; Tao Liu; Ruofeng Qiu; Wu Xie; Yunfei Qi

doi:10.1117/12.3035186

21 July 2024 Wordle difficulty prediction model based on PSO-LSTM and hierarchical clusters

Xiaolu Sun, Yueyue Fan, Xingyan Cai, Tao Liu, Ruofeng Qiu, Wu Xie, Yunfei Qi

Author Affiliations +

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132191P (2024) https://doi.org/10.1117/12.3035186
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

This paper focuses on word difficulty prediction and classification on the basis of capturing a year's word game data. Although Wordle was all the rage, recent tracking data shows that the Wordle popularity appears to be declining gradually, and the number of players has a downward trend. To promote the healthy development of Wordle and assist game developers in adjusting their game strategies in time, this paper establishes a word difficulty prediction model. Before Wordle difficulty prediction model is established, we preprocessed the data, rasterizing the area to be studied. In phase I, we introduced a PSO-LSTM prediction model and performed specific training, achieving an accuracy rate of over 85%. After coding the letters in “EERIE”, we successfully obtained the percentage of word “EERIE” on March 1,2023 will appear. The second phase uses hierarchical clustering analysis to subcategorize words by difficulty. The difficulty of words is then divided into five levels based on the folded graph of clustering coefficients derived from the elbow rule. Finally, this paper presents a model accuracy test and suggests the idea that this model can predict the data of serial relationship within the existence time and can be extended to the evaluation and weighted ranking under the influence of other factors.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaolu Sun, Yueyue Fan, Xingyan Cai, Tao Liu, Ruofeng Qiu, Wu Xie, and Yunfei Qi "Wordle difficulty prediction model based on PSO-LSTM and hierarchical clusters", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132191P (21 July 2024); https://doi.org/10.1117/12.3035186

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Data modeling

Mathematical modeling

Education and training

Particle swarm optimization

Performance modeling

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Mathematical optimization

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