# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
## 4 Experimental Results and Analysis
### Experimental Results and Performance Analysis  
First, to make the training and prediction of the model independent of each other, the data is divided into 80% training sets and 20% training sets. The feature extraction method TF-IDF of the AdaECELM model was adopted, and the minimum word frequency \(T_{f}\) and maximum inverse document frequency \(I_{df}\) were set to 5 and 0.8, respectively. Then, using the advantages of CS algorithm search, adaptive parameter search is carried out for the combined control hyperparameters of SVM [\(C\), \(gamma\)]. The maximum number of CS _Max_iterations_ is set to 100. The hyperparameter combination of the output sub-classifier is used for AdaECELM sentiment analysis. We compared eight classical machine learning models and compared the average results of 30 independent runs to ensure the comprehensive classification performance of each model [56]. The experimental results are shown in Table 2. In Table 2, the results with the highest accuracy are shown in bold, and the results with the second highest accuracy are shown in underline. In the imdbs data set, the accuracy of the eight comparison ML models is the highest at 73.50% of MNB, while that of the model in this paper is 76.83%, an increase of 3.33%. At the same time, compared with the second-ranked method on the other three data sets, the improvement was 1.9%, 0.54%, and 0.59%, respectively. In general, SVM and MNB are generally better. Compared with all models, AdaECELM has the highest accuracy, has certain effectiveness and robustness, and is a new baseline method worthy of consideration. Second, we also visualized precision, recall, and F1-score, as shown in Figures 4 and 5, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Imdbs** & **Yelp** & **Sen\_[PERSON]** & **Amazon\_Cells** \\ \hline LR & 0.7205 & 0.7782 & 0.7595 & 0.7895 \\ DT & 0.6277 & 0.7072 & 0.6002 & 0.7327 \\ RF & 0.6958 & 0.7618 & 0.6956 & 0.7785 \\ SVM & 0.7253 & 0.7820 & 0.7655 & 0.7958 \\ KNN & 0.6518 & 0.6453 & 0.5094 & 0.6502 \\ MNB & 0.7350 & 0.7675 & 0.7678 & 0.7873 \\ MLP & 0.7023 & 0.7513 & 0.7082 & 0.7683 \\ GBT & 0.7102 & 0.7610 & 0.6575 & 0.7832 \\
**AdaECELM** & **0.7683** & **0.8010** & **0.7732** & **0.8017** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results of different classification models (Accuracy). As shown in Figure 4, among the precision, recall, and F1-score indicators on two relatively small data sets of imdbs and yelp, the models with poor overall emotion classification performance include DT and KNN. LR, RF, SVM, MNB, MLP, and GBT are all on the same level as our model. However, from the comprehensive level, the classification correctness of the AdaECELM model and all F1 scores have been improved to varying degrees. In the amazon_cells data set shown in Figure 5, these metrics are consistent with the performance gains described in Figure 4 due to similarities in data size and problem-solving. However, when processing sen_pol, a relatively large data set, KNN showed a very serious classification fatigue, and DT, RF, MLP, GBT, and the other four methods were no longer at the same level as SVM and MNB. Compared with SVM and MNB, the two ML models have the best performance stability, but AdaECELM does not have a degree of index improvement. Second, because the text of imdbs data set is short, the wrong feature extraction of a certain word will have a great impact on the final fitting result of the model [56]. To this end, sensitivity analysis was carried out on the feature extraction process of the two parameters \(T_{f}\) and \(I_{df}\) of the word vector representation method, wherein \(T_{f}\) selection field was [2; 3; 4; 5; 6] and \(I_{df}\) selection field was [0.75, 0.80, 0.85, 0.90, 0.95]. The sensitivity analysis curve of adaptive search for different indicators of the AdaECELM model is shown in Figure 6.  
Figure 4: Precision\(\backslash\)Recall\(\backslash\)F1-score data comparison of imdbs and yelp.