## 3 Methods  
### Research Framework  
This study focuses on food products in the e-commerce domain. We collected data on 1062 products and their corresponding consumer reviews from Amazon.com's breakfast category, of which 672 entries include complete ingredient information. To analyze food-ingredient characteristics from the perspective of consumer preferences, the study employs two main approaches. The first is heterogeneous information network (HIN) representation, where we construct a heterogeneous food-ingredient information space using network representation techniques to capture deep semantic features of products and ingredients. The second is sentiment analysis of reviews, wherein we extract sentiments related to tastes and ingredients from each consumer review, generating adjusted reputation scores by excluding non-formulation factors. As an unsupervised study, we use the resulting embeddings and sentiment scores to examine how products and ingredients cluster within their respective embedding spaces and to analyze the distribution of high-reputation products and ingredients. The quality of our data analysis can also be demonstrated through semantic consistency and cluster interpretability. The overall research approach is illustrated in Figure 1.  
In analyzing the food-ingredient HIN with deep semantic information, we treat the inclusion and containment relationships between products and ingredients as reflecting food developers' considerations for ingredient compatibility, encompassing features of taste, texture, and nutrition. Building on this, the study constructs a heterogeneous information network of food ingredients, including nodes for both products (P1, P2, \(\ldots\)) and ingredients (I1, I2, \(\ldots\)), as shown in Figure 1. It then employs the heterogeneous information network representation learning algorithm metapath2 vec [57] to learn higher-order \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Research Element** & **Method** & **Reference Examples** & **Limitation** \\ \hline \multirow{6}{*}{Measuring of Consumer Satisfaction} & Surveys & [10; 11; 33] & Limited sample size, high \\  & Interviews & [13; 27; 34] & data-collection costs \\ \cline{2-4}  & \multirow{2}{*}{E-commerce Platforms} & \multirow{2}{*}{Ratings (Numerical)} & \multirow{2}{*}{[40; 41; 42]} & Possibly influenced by factors \\  & & & & unrelated to core product quality \\  & & & & (e.g., logistics, packaging) \\ \cline{3-4}  & & Review (Text) & [44; 45; 46; 47] & (Chosen method in this study) \\ \hline \multirow{3}{*}{Organization of Food and Ingredient Knowledge} & \multirow{3}{*}{Specialized Databases} & \multirow{3}{*}{[49; 50; 51; 52]} & Difficult to reflect relationships \\  & & & among ingredients that share \\ \cline{1-1}  & & & similar functions \\ \cline{1-1}  & & & or characteristics \\ \hline \multirow{3}{*}{Consumer Satisfaction Research for Food Formulation} & \multirow{3}{*}{Existing Studies: Consumer-Based Only} & \multirow{3}{*}{[48]} & Relies solely on keywords from \\ \cline{1-1}  & & & consumer reviews, making it \\ \cline{1-1}  & & & difficult to link directly to \\ \cline{1-1}  & & & specific ingredients \\ \cline{1-1}  & & & and formulations \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related literature. associations among network nodes, obtaining vector representations for both product and ingredient entities. This process yields a food-ingredient heterogeneous information space that captures deep semantic information in a food context. To adjust product reputation, we first manually summarize a list of keywords covering five topics (T1, T2,... in Figure 1) in consumer reviews: price, taste, ingredient, delivery, and pack. We then use the English sentiment lexicon SentiWordNet [58] to calculate sentiment scores for each sentence related to these five topics, creating a sentiment score matrix for each consumer. Based on the scores for taste and ingredient, we determine whether consumers approve of the food formulation.