## 2 Materials and Methods
### Feature Selection  
After text preprocessing and representation, the obtained feature words are still large in number, even after preprocessing and filtering, which leads to the high dimension of text representation vector. Therefore, it is important to use the feature selection algorithm to filter the feature words and thus reduce the dimension. Feature selection can not only reduce the computational complexity, but also help to improve the classification performance. In this paper, according to the limitation of the scale of the feature vector, the appropriate keyword range is selected, and the feature vector of the text is extracted and constructed according to the results of the keyword scanning. The construction of the feature vector is shown in Table 2.  
The classic feature selection algorithm _tf-idf_ adopts lexical statistical features as the feature set [36], and term frequency (TF) and inverse document frequency (IDF) are used to represent the words' importance in the text. The weight expression of feature words in the _tf-idf_ algorithm is shown below. \[w_{ij}=tf_{ij}\cdot idf=tf_{ij}\cdot\text{log}\bigg{(}\frac{N}{n}+0.01\bigg{)} \tag{2}\]  
where \(w_{ij}\) represents the weight of keyword \(t_{j}\) on classification \(C_{i}\) and describes the closeness between a keyword and this category; \(tf_{ij}\) represents \"word frequency\", that is, the sum of occurrence times in all documents in classification \(C_{i}\) of keyword \(t_{j}\); _idf_ represents \"anti-document frequency\", that is, the ratio between the number of documents and the number of documents containing keyword \(t_{j}\). \(N\) represents the number of documents, \(n\) represents \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Keyword 1 Feature** & \multicolumn{2}{c}{**Keyword 2 Feature**} & \multicolumn{2}{c}{**Keyword x Feature**} \\ \hline weight for & weight for & weight for & weight for & weight for \\ type 1 & type m & type 1 & type m & type 1 & type m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Construction of the feature vector. the total number of documents containing \(t_{j}\), and + 0.01 is used to prevent the situation where the weight is 0. However, the _tf-idf_ algorithm does not consider the distribution difference of words among different categories, and will give larger _idf_ values to common words that widely appear in each category and smaller _idf_ values to uncommon words concentrated in one category. Too low _idf_ values of feature words in a category will result in a weight that is too low. On the other hand, the _tf-idf_ algorithm ignores the distribution of words within the class. For example, some \"different\" keywords may appear in a category due to accidental factors, and within the document, the \"different\" keyword may appear frequently. In this case, the keyword should not represent the category. On the basis of analyzing some shortcomings of _tf-idf_ algorithm, algorithm _tf-DE_ is introduced. New statistics, Inter-Category Dispersion and Intra-Category Information Entropy, are introduced for feature selection [28]. A parameter is selected to describe the dispersion degree of the keyword distribution across multiple categories. Keywords with a more concentrated distribution in a certain category or a few categories are given higher values because such words obviously have better classification ability, while keywords evenly dispersed in each category without much classification representation ability should have relatively low values. Based on the statistical standard deviation formula, parameter \(D\) is defined as shown in the following. \[D(t)= \begin{cases}\frac{\sqrt{\frac{1}{\hbar}\sum_{i=1}^{m}\big{(}d_{i}(t)- \overline{d(t)}\big{)}^{2}}}{\overline{d(t)}},&\overline{d(t)}\
eq 0\\ 0,&\overline{d(t)}=0\end{cases}  \tag{3}\]  
where \(d_{i}(t)\) represents the number of documents containing keyword \(t\) in category \(C_{i}\), \(\overline{d(t)}\) represents the average value of \(d_{i}(t)\), and \(m\) represents the number of categories.