# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
###### Abstract
However, in multi-label scenarios, the most difficult task lies in assessing the reliability of the multi-label base learners, as it demands thorough comprehension of the interrelationships between different types of labels. Differentiating between diverse label learning within a multi-label context poses challenges since the behavior of the base learners depends on the entire set of labels rather than a single term [15]. This paper presents a unique methodology that leverages an evolving ensembler [16] and employs multi-label categorization on textual data, thereby attaining consistent enhancements in precision. The proposed method addresses the challenges previously encountered in multi-labeling text data. By incorporating a genetic algorithm with a support vector machine, improvements in various performance measures, including precision, have been observed. The suggested research demonstrates that merging DenseNet with the Aquila Optimizer (AO), i.e., the proposed method, yields promising results by significantly increasing quality performance while maintaining reasonable cost [16]. To further validate the study, comparisons are made between the newly developed model and five other classifiers, including stable discriminant analysis, decision tree, Convolutional Neural Network (CNN), maximum entropy, random forest, and generalized linear model. These comparisons confirm the superiority of the suggested methodology over all benchmark techniques [16]. The contributions are listed below in alphabetical order. * Introducing a novel approach that utilizes aspect-based multi-label classification for accurately classifying emotions in textual data, enabling more granular sentiment analysis. * Comparative Analysis of Sentiment Classification Techniques: Conducting an extensive analysis to compare the effectiveness of different sentiment classification techniques in categorizing emotions, providing insights into their suitability for multi-label sentiment analysis tasks. * Evaluation of State-of-the-Art Algorithms: Assessing the performance of five advanced multi-label classification algorithms on diverse emotion-based textual datasets, offering a comprehensive understanding of their strengths and weaknesses in handling multi-label sentiment analysis. * Introduction of Ensembler: Presenting the approach called Ensemble of DenseNet based on AO (EDAO) technique, which enhances the accuracy and variation of multi-label sentiment analysis models by integrating accuracy and diversity, offering a new perspective on multi-label instruction approaches. * Development of Comprehensive Workflow: Establishing a comprehensive workflow that incorporates preprocessing, feature extraction, and model tuning techniques, elevating the performance of sentiment analysis models and ensuring the use of refined and precise data. * Experimental Validation and Performance Comparison: Extensive experiments are being conducted to validate the effectiveness of the proposed EDAO approach. These experiments involve comparing the approach with existing benchmark methods and showcasing its superior ability to capture sentiment variations and handle complex multi-label datasets. These contributions significantly advance the field of multi-label sentiment analysis by introducing innovative approaches, conducting thorough evaluations, and providing valuable insights into sentiment classification model performance. The following is a breakdown of the manuscript's structure. Section 2 contains relevant work on MLL by other scholars in the area. The specifics of our conceptual framework for aspect-based multi-labeling, which utilizes a DenseNet-based ensembler, are described in Section 3. Section 4 describes the general framework. Section 4 of the study thoroughly presents the experimental results collected. Section 5 concludes with a summary of the findings and recommendations for further research.