# A Survey of the Applications of Text Mining for the Food Domain
## 3 Text Mining Overview
### Information Extraction  
Information extraction (IE) is a vital and dynamic field of research within NLP. Its main goal is to automatically extract structured information from unstructured text. Information extraction plays a crucial role in identifying entities, events, and relationships between entities in text, converting unstructured textual data into organized information. This pro \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Algorithms** & **Comments** & **Papers** \\ \hline \multirow{4}{*}{Dictionary-Based} & \multicolumn{3}{c}{Using a sentiment lexicon and} \\  & Sentiment Lexicon Matching & calculating based on the sentiment words in the text are simple and & \multirow{2}{*}{[PERSON] et al. (2015) [32],} \\  & Algorithm, Sentiment Intensity & effective, but rely on the quality of & \\  & Algorithm based on sentiment & the sentiment lexicon and are & \\  & lexicon, and so on. & unable to handle complex & \\  & & relationships in the context. & \\ \hline \multirow{3}{*}{Rules-Based} & \multirow{3}{*}{Keyword/Phrase Rule Matching Algorithm. and so on.} & Interpretable, domain-adaptable, & \multirow{3}{*}{[PERSON] et al. (2015) [35]} \\  & & but rule-engineering dependent, & \\  & Algorithm. and so on. & difficulty handling complex & \\  & & contexts. & \\ \hline \multirow{3}{*}{Machine Learning} & \multirow{3}{*}{KNN, SVM, NB, and so on.} & Adaptable to handle complex & \multirow{3}{*}{[PERSON] et al. (2020) [36]} \\  & & language structures and contexts, & \\ \cline{1-1}  & & but overly dependent on data & \\ \hline \multirow{3}{*}{Deep Learning} & \multirow{3}{*}{CNN, RNN, BERT, and so on.} & Capable of learning more complex & \multirow{3}{*}{[PERSON] et al. (2020) [37]} \\ \cline{1-1}  & & semantic and contextual features, & \\ \cline{1-1}  & & but requires large computational & \\ \cline{1-1}  & & resources and training data. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: General methods of sentiment analysis. cess provides valuable assistance for knowledge acquisition and text analysis [15]. Table 3 presents commonly used methods for information extraction, outlining their advantages and disadvantages, along with references to papers that employ these methods. Information-extraction technology plays a crucial role in the extensive application within the food industry. This technology is utilized to analyze ingredient lists and nutritional labels of food products, extracting key information such as names, carbohydrates, proteins, fats, and vitamins [38; 39]. This capability is essential for monitoring food components and understanding the nutritional composition of different food items. Furthermore, information extraction contributes to real-time monitoring of food safety, enabling regulatory authorities to extract relevant information related to food safety incidents from sources like news reports and social media. This empowers regulatory bodies to swiftly identify potential risks and take timely actions to ensure public health and safety [38; 39]. For example, [PERSON] et al. [42] utilized artificial intelligence to detect novel stimulants in food supplements, employing machine learning to identify 20 new stimulants from the scientific literature and online sources. Through word embeddings and text mining techniques, relevant authorities can proactively discover potential health risks and promptly alert consumers. Additionally, information-extraction technology is applied to establish relationships between food and chemicals by analyzing the biomedical peer-reviewed scientific literature [43]. Information-extraction technology has evolved from basic methods to sophisticated approaches. Initially, information extraction relied on manually crafted rules and templates. In the 1990s, there was a shift towards employing statistical machine learning models, reducing the need for extensive manual labor in feature engineering [41]. In the 21 st century, the introduction of deep learning technologies has further propelled the development of information-extraction methods.