THE LIFECYCLE CHANGE MODEL BASED ON WORDLE ANALYSIS AND STUDY

Description

The research paper presents a detailed analysis of the Wordle game dataset, focusing on various aspects such as the lifecycle change model, the effect of attributes on scores reported in Hardmode, prediction analysis using the GloVe-LBP neural network model, and the K-Means and Z-score classification models. The study introduces an exponential function to fit data for different stages and establishes a lifecycle change model to describe the growth phases over time. It investigates the impact of repeated letters in words on the difficulty level in the game, categorizing words into repeated letter and no repeated letter attributes. The paper utilizes the GloVe model to convert words into 25-dimensional word vectors for input to the LBP neural network model, which is trained using mean squared error and percentage labels. Additionally, K-Means and z-score classification models are employed to evaluate word difficulty based on the ratio of guesses to total attempts, providing insights into the difficulty levels of words in the dataset.