# Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis
###### Abstract  
Standard machine learning and deep learning architectures have been widely used in the field of sentiment analysis, but their performance is unsatisfactory if the input texts are short (e.g., social media posts). Specifically, the accuracy of standard machine learning methods crucially depends on the richness and completeness of the features used to represent the texts, and in the case of short messages, it is often difficult to obtain high-quality features. Conversely, methods based on deep learning can achieve better expressiveness, but these methods are computationally demanding and often suffer from over-fitting. This paper proposes a new adaptive evolutionary computational integrated learning model (AdaECELM) to overcome the problems encountered by traditional machine learning and deep learning models in sentiment analysis for short texts. AdaECELM consists of three phases: _feature selection_, _sub classifier training_, and _global integration learning_. First, a grid search is used for feature extraction and selection of term frequency-inverse document frequency (TF-IDF). Second, cuckoo search (CS) is introduced to optimize the combined hyperparameters in the sub-classifier support vector machine (SVM). Finally, the training set is divided into different feature subsets for sub-classifier training, and then the trained sub-classifiers are integrated and learned using the AdaBoost integrated soft voting method. Extensive experiments were conducted on six real polar sentiment analysis data sets. The results show that the AdaECELM model outperforms the traditional ML comparison methods according to evaluation metrics such as accuracy, precision, recall, and F1-score in all cases, and we report an improvement in accuracy exceeding 4.5%, the second-best competitor. texts have poor performance because it is hard to manually obtain high-quality features to represent short texts [4; 5]. In contrast, deep learning models often incur overfitting and suffer from high computational costs [6]. We point out that a better feature extraction scheme coupled with a better machine learning model could significantly improve the performance of an SA system designed to work on short texts. On the one hand, in fact, advanced text feature extraction schemes can more accurately express semantic information in short text data [7]. On the other hand, a more refined machine learning model can obtain very accurate results by using a small amount of computing resources with the advantage of exploiting a relatively simple model structure [8; 9]. Previous research work on SA for short texts applied standard machine learning models such as support vector machine (SVM), mainly due to their ability to deal with nonlinear relationships [10; 11]. [PERSON] et al. [12] found that SVM had better classification accuracy than other classifiers when feature redundancy was small. In the context of Arabic text classification, [PERSON] et al. [13] proposed a hybrid model combining the firefly feature selection scheme and SVM to perform text classification; the proposed system achieved an accuracy of 99.40% on OSAC data set. [PERSON] et al. [14] reviewed the development of machine learning and deep learning methods for NLP tasks in the past decade, especially in text sentiment classification models and feature extraction methods. [PERSON] et al. [14] pointed out that DL models have achieved great success in several NLP tasks, but the advantages of linear classifiers such as SVM in some text mining tasks cannot be ignored [15]. However, the classical SVM model does not perform well in most text analysis, and integrating other mechanisms, strategies, and schemes is useful. [PERSON] et al. [16] used a locust optimization algorithm to select the optimal values of SVM parameters and formed a classifier with strong adaptability for intelligent fault diagnosis of rotating machinery. [PERSON] et al. [17] adopted the same optimization idea to propose the WOA-SVM model, which achieved an accuracy of 95.65%.