Enhanced ensemble learning for aspect-based sentiment analysis on multiple application oriented datasets

The main goal of this article is to develop and propose a novel ABSA method using enhanced ensemble learning (EEL) with optimal feature selection. Initially, the data from multiple applications is gathered and subjected to the preprocessing by 'stop word removal and punctuation removal, lower case conversion and stemming'. Then, the aspect extraction is done by separating 'noun and adjective and verb and adverb combination'. From this, the 'Vader sentiment intensity analyzer' is used to capture the weighted polarity feature, and then, the word2vector and 'term frequency-inverse document frequency' are extracted as features. The optimal feature selection using best and worst fitness-based galactic swarm optimization (BWF-GSO) is used for selecting the most significant features. With these features, ensemble learning with different classifiers like 'recurrent neural network, support vector machine and deep belief network' performs for handling the sentiment analysis with parameter optimization. The suggested models are helpful and generate better than the existing outcomes, according to experimental data. Through the performance analysis, the accuracy of BWF-GSO-EEL was 1.16%, 1.58%, 2.01% and 1.37% better than FF-MVO-EEL, FF-EEL, MVO-EEL and PSO-EEL, respectively. Thus, the promising performance has been observed while comparing with other algorithms.