## 3 Research Methods
### Data Analysis
#### 3.4.4 Phase Four: Sentiment Association Mining via Co-Occurrence Network Analysis (CONA)  
Following DASA, data filtration and subsequent CONA on polarized data facilitated the discovery of associations between sentiment tendencies and multiple dimensions, thereby aiding in the optimization of product design and the enhancement of user satisfaction. This CONA method, with extensive applications in graph-based NLP, introduced by [PERSON], [PERSON], and [PERSON] [56], encompasses key object extraction [53] and word sense disambiguation [57]. Co-occurrence is defined by the closeness of two words within a document, recognized when the distance between them is less than a predetermined window size [58]. The co-occurrence matrix captures word vectors, detailing their co-occurrence patterns with other words. The Jaccard similarity coefficient [59] measures the similarity between word pairs, indicating the strength of connections between keywords. Centrality indicators, such as eigenvector centrality (EC) [60], evaluate a node's importance by considering its connections and the influence of adjacent nodes. A co-occurrence matrix was used to quantify the strength of keyword associations and visualize them in a graphical network, where nodes represent keywords and edges indicate their relationships. The procedure was as follows:  
Step 1. Data preparation: we reviewed sentiment polarity data and extracted representative keywords, focusing on clearly polarized (positive or negative) entries to ensure relevancy. Step 2. Co-occurrence matrix construction: we calculated the co-occurrence frequency for each keyword pair (\(C_{ij}\)) using (15):  
\[C_{ij}=\frac{1}{N}\sum_{k=1}^{N}A_{ik}A_{jk} \tag{15}\]  
where \(N\) is the text count, and \(A_{ik}\) signifies the presence of keyword \(i\) in text \(k\) (1 if present and 0 otherwise). Step 3. Matrix normalization: we adjusted the co-occurrence matrix to standardize keyword frequency dimensions, as illustrated by Equation (16):  
\[M_{ij}=\frac{C_{ij}}{\sqrt{C_{i+}\cdot C_{j+}}} \tag{16}\]where \(M_{ij}\) represents the normalized frequency, \(C_{ij}\) denotes the raw frequency, and \(C_{i}\) and \(C_{j}\) represent the total co-occurrence frequencies of keywords \(i\) and \(j\), respectively. Step 4. Network graph construction: This step translated the co-occurrence matrix into a graphical representation, symbolizing words as nodes and co-occurrence relationships as edges. The weight of the edges was determined by the frequency of word co-occurrence. Step 5. Network analysis: computation of the EC and Jaccard similarity indices was executed to assess keyword importance and co-occurrence strength, calculated using the subsequent Equation (17):  
\[EC_{i}=\frac{1}{\lambda}\sum_{j=1}^{N}A_{ij}EC_{j} \tag{17}\]  
where \(EC_{i}\) is the centrality of node \(i\), \(A_{ij}\) signifies an edge between nodes \(i\) and \(j\), and \(\lambda\) is the eigenvalue. The Jaccard contribution (JC) similarity index was determined using Formula (18):  
\[JC_{ij}=\frac{|N(i)\cap N(j)|}{|N(i)\cup N(j)|} \tag{18}\]  
where \(N(i)\) and \(N(j)\) are the sets of neighboring nodes for keywords \(i\) and \(j\), respectively. Step 6. Comprehensive weight calculation: The calculation of comprehensive weight integrates multiple indicators, standardizing and aggregating their weighted values to measure keyword importance. It was obtained by normalizing and summing the weighted values of the EC, JC, and term frequency (TF), with the following Formulas (19) and (20):  
\[X^{\prime}=\frac{X-X_{\min}}{X_{\max}-X_{\min}} \tag{19}\]  
where \(X\) is the raw value, \(X^{\prime}\) is the normalized value, and \(X_{\min}\) and \(X_{\max}\) are the minimum and maximum values of the indicator, respectively.