# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Sentiment Analysis  
Sentiment analysis, also known as Opinion Mining, is a prominent and extensively researched field within NLP and textual analysis. From the NLP perspective, the core objective of sentiment analysis lies in the extraction of both the subject of the comment and the proclivity of the commenter to convey sentiment towards said subject [15]. In Table 2, commonly used methods for sentiment analysis, along with their advantages and disadvantages, are presented alongside relevant papers that employ these methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Algorithms** & **Comments** & **Papers** \\ \hline \multirow{6}{*}{Deep learning} & & Has contextual understanding & [PERSON] et al. (2019) [24], \\  & & advantage, learns rich semantic & [PERSON] et al. (2020) [25], \\  & & knowledge, but has high data & [PERSON] et al. (2021) [26], \\  & & requirements and & [PERSON] et al. (2022) [27], \\  & & computational complexity. [PERSON] et al. (2022) [9], \\  & & & [PERSON] et al. (2023) [28] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._In the food domain, sentiment analysis technology is widely applied. When assessing entities such as fast-food restaurants, bubble tea shops, and various food products, sentiment analysis technology enables the detailed collection, categorization, and analysis of online consumer feedback and reviews [34]. This analysis not only aims to ascertain whether consumers' evaluations tend towards positivity or negativity, but also, through meticulous research, can pinpoint consumers' specific perspectives on aspects such as taste, texture, packaging, pricing, and other dimensions of food [33].  
Sentiment analysis has progressed from rules-based methods to statistical machine learning and, more recently, deep learning. Initially, it relied on dictionaries and rules, then evolved to incorporate machine learning classifiers for improved results. These technologies not only provide businesses with profound insights into consumer preferences, but also directly impact product design and marketing strategies [35; 37]. For instance, [PERSON] et al. [32] conducted a rigorous analysis using a vast dataset from Facebook, creating a social network model that offered valuable insights into market dynamics, competitiveness, and segmented markets.  
The introduction of neural network models and the utilization of sentiment analysis techniques employing word and sentence vectors enable the autonomous extraction of advanced semantic features, showcasing adaptability across various data domains and language differences. Taking [PERSON] et al.'s [36] research as an example, they employed a stacked ensemble learning framework, integrating various base learners such as Naive Bayes, Support Vector Machine, XGBoost, FastText, a convolutional neural network, Long Short-Term Memory, and BERT for evaluating the impact of global food safety news.  
Sentiment analysis technology has garnered significant attention in the food industry, progressing from traditional machine learning to the developments in deep learning. This advancement not only propels progress in the food domain, but also stimulates the evolution of related NLP.