## 3 Methodology
### Topic Model  
Topic model is an unsupervised generation model, which is widely used in word frequency analysis and text classification. The latent Dirichlet allocation (LDA) proposed by [PERSON] et al. [23] is one of the most classic topic models [24]. This study considered the background information of each document in text as important for research analysis. Incorporating the background information into the model as covariates can effectively improve the performance of the model and better understand the model results. The LDA allows researchers to analyze the relationship between a topic and the background  
Figure 3: The 5S Pyramid Model.  
Figure 2: Five steps of EBM.  
information of a text. Therefore, the LDA was employed as a probabilistic topic model for this research to better explore the latent topics in the text data.  
Latent Dirichlet allocation (LDA) is a generative probabilistic model of a corpus. The basic idea is that documents are represented as random mixtures over latent topics, where each topic is characterized by a distribution over words.  
LDA assumes the following generative process for each document w in a corpus D:  
1. Choose N - Poisson(\(\xi\));
2. Choose \(\theta\) - Dir(\(\alpha\));
3. For each of the N words w_n: 1. Choose a topic z_n - Multinomial(\(\theta\)); 2. Choose a word w_n from p(w_n 1 z_n,\(\beta\)), a multinomial probability conditioned on the topic z_n.  
Several simplifying assumptions are made in this basic model, some of which we remove in subsequent sections. First, the dimensionality \(k\) of the Dirichlet distribution (and thus the dimensionality of the topic variable \(z\)) is assumed known and fixed. Second, the word probabilities are parameterized by a k \(\times\) V matrix \(\beta\) where \(\beta_{ij}=p(w_{j}=1\,|\,z_{i}=1)\), which for now we treat as a fixed quantity that is to be estimated. Finally, the Poisson assumption is not critical to anything that follows, and more realistic document length distributions can be used as needed. Furthermore, note that N is independent of all the other data generating variables (\(\theta\) and \(z\)). It is thus an ancillary variable, and we will generally ignore its randomness in the subsequent development.  
A k-dimensional Dirichlet random variable \(\theta\) can take values in the (\(k-1\))-simplex (a \(k\)-vector \(\theta\) lies in the (\(k-1\))-simplex if \(\theta_{i}\geq 0\), \(\sum k\) \(i=1\)\(\theta_{i}=1\)), and has the following probability density on this simplex:  
\[p(\theta|\alpha)=\frac{\Gamma\left(\sum_{i=1}^{k}\alpha_{i}\right)}{\prod_{i= 1}^{k}\Gamma(\alpha_{i})}\theta_{1}^{\alpha_{1}-1}\ldots\theta_{k}^{\alpha_{ k}-1} \tag{1}\]  
where the parameter \(\alpha\) is a \(k\)-vector with components \(\alpha_{i}>0\), and where \(\Gamma(x)\) is the gamma function. The Dirichlet is a convenient distribution on the simplex--it is in the exponential family, has finite dimensional sufficient statistics, and is conjugate to the multinomial distribution. In Section 5, these properties will facilitate the development of inference and parameter estimation algorithms for LDA. Given the parameters \(\alpha\) and \(\beta\), the joint distribution of a topic mixture \(\theta\), a set of N topics \(z\), and a set of \(N\) words \(w\) is given by  
\[p(\theta,z,w|\alpha,\beta)=p(\theta|\alpha)\prod_{n=1}^{N}p(z_{n}|\theta)p(w_ {n}|z_{n},\beta) \tag{2}\]