# Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining
## 2 Related Research
### Analysis of Educational Data USING Text Mining  
Text mining has emerged as an effective tool for analyzing textbooks. AI-driven natural language processing technologies enable frequency analysis of large amounts of text data in textbooks through the Term Frequency-Inverse Document Frequency (TF-IDF) method. Additionally, significance, concordance, and relevance can be objectively calculated through topic modeling using Latent Dirichlet Allocation (LDA). Text mining is a method that extracts semantic information by effectively processing unstructured data. It falls within the realm of natural language processing, a subfield of AI, and aims to uncover associations between topics and concepts by analyzing linguistic connections within textual data. Through quantitative analysis of word types, frequencies, and semantic relationships in large volumes of text, text mining unveils the characteristics and hidden patterns within the data [30; 31; 32]. This approach enables the objective analysis of educational data from multiple perspectives, shedding light on research topics and trends. Two key techniques in text mining are frequency analysis using TF-IDF and topic modeling using LDA [33; 34; 35]. TF-IDF is a statistical method that assigns weights to words in a document-term matrix (DTM) based on their importance. The TF component represents the total frequency of words in the document, whereas the IDF part indicates the number of documents in which each word appears, highlighting frequently used words [33]. TF-IDF combines these values to assign weights to words in the document, as follows:  
* TF-IDF (t, d, D) = TF (t, d) \(\times\) IDF (t, D). \[w_{x},y=tf_{x,y}\times\log\left(\frac{N}{df_{x}}\right)\]
* TF (t, d) = (number of occurrences of term t in document d)/(total number of terms in document d). * IDF (t, D) = ln (total number of documents in the corpus)/(number of documents with term t). Topic modeling is commonly employed for topic analysis and trend research in specific domains. It assumes that each text document comprises multiple topics and clusters the associations between words within the document to uncover the underlying semantic structure. This method facilitates the extraction of topics from the text and identifies the most relevant terms associated with each topic [34; 35]. One of the various topic modeling methods available is LDA, which is a probabilistic graphical model proposed by [PERSON] et al. (2003) [36] that estimates the probability of word inclusion in a particular topic using a Dirichlet distribution. In LDA, a corpus, which is the collection of documents under analysis, is assumed to consist of multiple topics. The distribution of words corresponding to each topic within the corpus determines the topics assuming that the words inferring a latent topic are fixed. This method groups correlated words into a single topic, enabling the inference of latent topics based on words assigned to the same group [36; 37]. The concept of LDA was presented in Figure 1: the analyzed corpus comprises M documents, with N representing the number of words in each document, \(\omega\) denoting a word vector, and z being the topic assigned to a specific word within the document. The latent parameter \(\omega\) defines the topic distribution of each document following a Dirichlet distribution, and words (\(\omega\)) related to topics are allocated using Bayesian posterior probability extraction. \(\alpha\) is an external parameter that adjusts the topic distribution \(\theta\) of each document, and \(\beta\) is an external parameter that adjusts the word \(\omega\) of each topic. Equation (1) illustrates the estimation of the latent parameters (\(\theta\), \(z\)) for topic extraction. It can also be expressed using variational parameters \(\theta\) and \(\theta\) through a Bayesian posterior probability distribution, as shown in Equation (2). Here, \(\gamma\) is a Dirichlet parameter, and \(\gamma\) is a polynomial parameter (\(\varphi 1\),..., \(\varphi N\)).