## 4 Methodology
### Methods and Procedure
#### 4.3.2 Coh-Metrix Analysis and Unsupervised Learning PCA and VADER  
To supplement the walkthrough method, computational measures from Coh-Metrix were employed to extract structural metrics that elucidate narrative coherence and linguistic variance within official game materials. Furthermore, principal component analysis (PCA), an unsupervised learning technique, was applied to systematically explore and visualize the underlying structural dimensions across game generations. VADER sentiment analysis was incorporated to extend the structural characterization, delving into emotional tendencies across different game versions. Finally, the above results explained the narrative structure of official materials (RQ9). **Data Collection and Preprocessing**  
This study involved gathering official campaign videos and reports released regarding each Animal Crossing generation (see Table 2). We systematically transcribed the multimodal data into a unified textual corpus (Corpus1) for computational analysis. Text data underwent a preprocessing phase to ensure coherence, eliminating extraneous elements like special characters, HTML tags, and inconsistent formatting. In accordance with [PERSON] et al. 's [42] protocol on data validation, cleaning, and verification, we used natural language processing (NLP) techniques to optimize data integrity. The corpus was segmented into narrative units by punctuation in order to maintain structural coherence while aligning with linguistic norms [42; 43]. Following the established protocols [44; 45], text preprocessing included the removal of non-linguistic elements (e.g., hyperlinks, timestamps, special characters), normalization procedures (e.g., lowercasing, removing redundant spacing), and segmentation into narrative units guided by punctuation marks to preserve semantic coherence [42; 43]. Afterwards, the corpus was processed by Coh-Metrix linguistic analysis. Then, Coh-Metrix-generated linguistic metrics were normalized prior to PCA to reliably identify principal components. Furthermore, considering VADER's sensitivity to informal textual nuances and emotional expressions, preprocessing carefully retained context-specific \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Official Campaign Videos** & **Official Reports** \\ \hline Count & 5 & 32 \\ \hline Median length of the texts & 280 words & 316 words \\ \hline \multirow{2}{*}{Length of the source} & 13.35 min/1400 words & \multirow{2}{*}{10,136 words} \\  & (transcription) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The fundamental details of the original texts and the transcribed target texts. terms, emotive language, and informal stylistic elements, thus enabling accurate sentiment analysis across the corpus. **Pipeline for Unsupervised learning PCA of Coh-Metrix**  
Coh-Metrix facilitates a quantitative comparison of our qualitative findings, enabling a systematic examination of linguistic structure across game versions. Coh-Metrix analyzes text from five aspects: narrativity, deep cohesion, referential cohesion, syntactic simplicity, and word concreteness [44; 46; 47; 48]. As specified by our research objective, we selected dimensions related to Descriptive Metrics, Text Easability Principal Component Scores, Referential Cohesion, LSA (Latent Semantic Analysis), Lexical Diversity, Connectives, Situation Model, Syntactic Complexity, Syntactic Pattern Density, and Readability, each comprising multiple indices (details in Appendix A, Table A1) [48]. Compared with previous approaches measuring words' correlations with sentences and legibility, selected measures of Coh-Metrix evaluate their performances in the whole cognitive process, consistent with our focus on syntactic construction [45; 49]. After data preprocessing, we first applied Coh-Metrix 3.0 software to analyze the discourse transcription (N = 37). We measured 86 indices belonging to 10 dimensions (Table A1) to predict language features, cohesion, and rhetorical compositions, standardizing all measures to prevent bias in subsequent algorithms.