Word game prediction and difficulty classification based on machine learning

Wenzhe Zhang

doi:10.1117/12.3003958

11 October 2023 Word game prediction and difficulty classification based on machine learning

Wenzhe Zhang

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128005J (2023) https://doi.org/10.1117/12.3003958
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. Based on the research objectives, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the (Autoregressive Integrated Moving Average-Support Vector Machine) ARIMA-SVM model was proposed. The results showed that the ARIMA-SVM model performed about 17% better than the ARIMA model. To address the problem of predicting the number of attempts, the Random Forest Regression model was proposed. In regard to the problem of classifying word difficulty, the GA-FCM clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes, and the experimental results showed that (Genetic Algorithm-Fuzzy C-Means) GA-FCM performed significantly better than the FCM model in all indicators.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenzhe Zhang "Word game prediction and difficulty classification based on machine learning", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128005J (11 October 2023); https://doi.org/10.1117/12.3003958

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available