Adaptive Evolutionary Computing Ensemble Learning Model for Sentiment Analysis

Author:

Liu Xiao-Yang1ORCID,Zhang Kang-Qi1ORCID,Fiumara Giacomo2ORCID,Meo Pasquale De3,Ficara Annamaria4ORCID

Affiliation:

1. Department of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China

2. Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, 98166 Messina, Italy

3. Department of Ancient and Modern Civilizations, University of Messina, 98168 Messina, Italy

4. Department of Cognitive Science, Education and Cultural Studies, University of Messina, 98121 Messina, Italy

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.

Funder

Key Project of Chongqing Municipal Education Commission

Graduate Innovation Fund of Chongqing

Publisher

MDPI AG

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