# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
## 3 Proposed Method
### AdaECELM Model
\[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]2  
The nonlinear decision function obtained from the above equation is shown in Equation (9). \[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]3  
The kernel function \(K\) in Equation (8) is the radial basis kernel function (Gaussian kernel function, RBF), and the expression is  
\[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]4  
The SVM classification process includes data set partitioning, data preprocessing, SVM model construction, combination hyperparameters selection for training, model performance evaluation, and classification accuracy output. SVM will use CS to optimize it to obtain an adaptive optimal hyperplane partition hybrid classifier. This greatly improves the performance of the model and gives it stronger adaptive abilities than traditional manual parameter tuning and random parameter optimization strategies. The CS+SVM execution pseudocode is shown below. ```
Input: Range of penalty coefficient \(C\) and RBF nuclear coefficient gamma to be searched Output: Result of adaptive hyperparameter combination 1 CS Parameters: 2 // Maximum number of iterations, population size, discovery probability, step size adjustment 3 Initialization: 4 // Total number of nests, number of iterations, discovery probability, dimension, step size 5 for \(t\) in Max iterations do // \(t\) is the current iteration of the CS 6 for \(i\) in Np do //Np is the population size of the CS 7 Levy Flight Updates: \(Levy(\beta)\sim\frac{q\times q}{|v|^{1/p}}\) 8 Regeneration of next generation population 9 Discovery probability: Reset abandoned nest 10 Population renewal: \(x_{i}^{t+1}=x_{i}^{t}+rand\times(x_{i}^{t}-x_{i}^{t})\) 11 Update hyperparameter combination [\(C\), gamma] 12 return the hyperparameter combination with the highest score
```  
**Algorithm 1** CS+SVM parameters optimization  
The corresponding sub-classifier can be trained by CS-SVM. Finally, the AdaBoost method is applied to combine the predictions generated by each sub-classifier. The category prediction confidence of feature \(F_{i}\) can be expressed as Equation (12). \[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]5  
The class \(z\) of feature \(F_{i}\) is \(C_{z}\). \(H_{zt}\) (\(F_{i}\)) is the result predicted by the \(t\)-th sub-classifier for class \(z\). Thus, the \(F_{i}\) prediction results integrated with AdaBoost can be obtained, as shown in Equation (13). \[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]6  
Algorithm 1 summarizes the CS-SVM sub-classifier procedure of the AdaECELM model. In the time complexity analysis of the AdaECELM model, the time complexity we need to consider is divided into the sum of the complexity of three modules: feature extraction, model training, and classification prediction. Let a data set have \(N\) samples, and then the time complexity of the feature extraction process is O(\(N\)), and the resulting compressed feature vector dimension is \(D\). When SVM classification is adopted, \(M\) support vectors are required, and the required computation amount is \(M^{2}\). In this case, the training time complexity is O(\(N_{train}\),\(M^{2}\)), and the test time complexity is O(\(N_{test}\)). Therefore, the time complexity of the text data set using SVM is O (\(N+N_{train}\)\(M^{2}+N_{test}\)D). For the training stage of a sub-classifier in AdaECELM, the sample is \(N/3\), the support vector is \(M\), and the RBF hyperparameter combination needs to be optimized by the \(k\)-order CS optimizer. In this case, the time complexity of the training phase is O(\(k(N_{train}/3)\)\(M^{2}\)), then the final time complexity is O (\(N+k(N_{train}/3)\)\(M^{2}+N_{test}\)D). From the theoretical stage, compared with the traditional SVM model, when the number of CS iterations in the AdaECELM model is 3, the computational efficiency of the two models is exactly the same. It should be noted that the number of CS iterations is a fixed parameter.