# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Text Clustering  
Text clustering is an unsupervised machine learning paradigm that autonomously categorizes unstructured textual data based on inherent similarities [15]. Its primary utility lies in its ability to reveal underlying themes and concepts within textual corpora, condensing extensive datasets by identifying textual resemblances and grouping similar compositions into coherent clusters. Unlike supervised learning, text clustering does not require manual data labeling, making it particularly effective for managing large textual archives. Common methodologies include distance-based clustering techniques, such as hierarchical clustering algorithms, partitioning algorithms, and hybrid strategies that combine hierarchical and partitioned clustering approaches [15]. Table 5 presents commonly used methods for text clustering, outlining their advantages and disadvantages, along with references to papers that employ these methods.  
Text clustering has gained significant traction within the food domain, particularly in endeavors such as the extraction and categorization of themes from consumer feedback. This application enhances the ability to analyze and predict consumer preferences and tendencies related to food, utilizing social network-based consumer ratings and feedback [58]. An interesting application involves the use of a fuzzy relational clustering algorithm to classify Korean food items based on the adjectives used to describe their flavors. This approach resulted in the establishment of linguistic scales for various gastronomic categories, benefiting consumers in selecting their preferred foods and providing valuable insights to purveyors and producers about consumer requisites and inclinations [57]. Leveraging text-clustering technology, food enterprises gain the capability to extract meaningful insights from textual datasets, enlightening them about consumer needs and the dynamics of public sentiment [56].  
As an unsupervised text analytical modality, text clustering serves as a pivotal instrument for the classification and scrutiny of extensive unstructured textual data. Its intersection with the food landscape continues to grow and evolve alongside advances in machine learning and natural language processing. Consequently, it provides the alimentary industry with an indispensable tool for consumer demand analysis and public opinion monitoring, emerging as a guiding star in the arena of data analytics [55].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Algorithms** & **Comments** & **Papers** \\ \hline \multirow{6}{*}{Hierarchical Clustering} & \multirow{6}{*}{\
\begin{tabular}{c} KNN, Complete-Linkage \\ Clustering, and so on. \\ \end{tabular} } & No need to pre-specify the number of clusters, able to capture hierarchical structure, but higher computational complexity for large-scale datasets, more sensitive to outliers and noise. [PERSON] et al. (2018) [55], [PERSON] et al. (2019) [56] \\ \hline \multirow{2}{*}{Non-Hierarchical Clustering} & \multirow{2}{*}{
\begin{tabular}{c} K-Means Clustering, \\ Grid-Based Clustering, and \\ so on. \\ \end{tabular} } & Computationally efficient for irregularly shaped clusters, but sensitive to initial values. [PERSON] et al. (2013) [57], [PERSON] et al. (2018) [58] \\ \hline \hline \end{tabular}
\end{table}
Table 5: General methods of text clustering.