# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
## 3 Proposed Method
### AdaECELM Model  
This section introduces the AdaECELM model for sentiment analysis of short texts. Figure 1 shows the overall framework of the model. The AdaECELM model mainly preprocesses text from TF-IDF feature extraction, feature selection, and feature compression standardization. Then, the text features are divided into multiple training sets and test set (we considered three training subsets in this paper). A sub-classifier is constructed to train the classifier over each available training subset and optimize the combination parameters. Finally, AdaBoost integrates the three trained sub-classifiers to perform sentiment analysis for short texts. In the AdaECELM model, word segmentation and feature extraction are carried out, and, more specifically, TF-IDF is used to extract word vector features. The TF-IDF model is shown in Equations (1) and (2). \[T_{f}(w,D)=\frac{f_{wD}}{N_{D}} \tag{1}\]  
\[I_{df}(w,D)=1+\log\frac{N}{1+df(w)} \tag{2}\]  
where \(T_{f}\) represents the frequency of \(w\) in text \(D\), and \(f_{wD}\) and \(N_{D}\) represent the number and total number of words \(w\) in text \(D\), respectively. \(I_{df}\) represents the inverse document frequency of \(w\), \(N\) is the total number of data, and \(df\) represents the total number of \(w\) words in \(N\). Due to the difference in the size of different data sets, it is necessary to select the maximum document word frequency and minimum inverse document frequency of TF-IDF. The AdaECELM uses grid search to extract the features of different data sets. The extracted features are highly sparse, which is not conducive to further machine learning training and prediction. To save computing resources, accurately express text features, and reduce the redundancy of features, we carry out intensive transformation of this sparse matrix. The conversion process is shown in Figure 2.  
Figure 1: Architecture of AdaECELM for sentiment analysis. The processed features are divided into three training subsets and a test set. In the sampling process, text features are first divided into a training set and a test set according to a certain ratio. The training set is then randomly divided into three different training sets. To ensure that the text distribution is better divided into different training subsets to increase the diversity of the training set, we randomly constructed each training subsets by sampling data from the original subset; to this end, data from the training set were sampled through a normal distribution with mean equal to zero and standard deviation equal to one A sub-classifier is trained for each training subset. At this time, the cuckoo search (CS) algorithm was used to optimize the hyperparameters of SVM. Cuckoo search draws inspiration from the parasitic breeding behavior of cuckoo [33]. Cuckoos lay their eggs in other nests and use other nests to hatch, but there is a risk of detection by the host. To cope with this risk, cuckoo learns the songs of other young birds, drawing on this feature, and designs a CS algorithm. The proposed algorithm is based on three rules: one egg is laid each time, nests are randomly selected for incubation, and optimal nests are retained until the next iteration, with a fixed number of nests and a probability of discovery. The algorithm simulates the local search of the host discovery behavior through the global search of [PERSON]'s flight mechanism, evaluates the merits of the function fitness, and ensures the optimal solution set of the surviving individuals. By integrating these elements, the cuckoo search algorithm can efficiently find the optimal solution. The model using the Levy Flights mechanism is as follows:  
\[{x_{i}}^{t+1}={x_{i}}^{t}+\alpha\oplus Levy(\beta) \tag{3}\]  
According to Equation (3), at the \(t\) iteration, the global search update formula is obtained by the \(i\)-th individual by multiplying [PERSON]'s random search path Levy (\(\beta\)) by a step point \(a\) (with step size ranging from 0 to 1).