# An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification
###### Abstract
In sentiment analysis, classification typically involves binary methods for sentiment polarity (positive or negative) or multi-class methods for categorizing emotions into multiple classes. This distinction sets sentiment analysis apart from other techniques, as it requires specialized approaches to handle the complexity of emotions expressed in a text. This paper proposes a multi-label labeling strategy to categorize emotions, encompassing a diverse range of emotional aspects of the human mind. The impact of sentiment analysis is evident in various domains. For instance, in one study [5], a multi-task, multi-label classification approach was proposed to integrate attitude categorization using subject tweets and sentiment analysis. Although the sentiment analysis task was framed as a multi-label problem, where multiple emotions could be associated with a single text, the study utilized data that allowed for a wide range of emotional reactions. Similarly, another article [6] classified emotions by categorizing news articles using labels and phrases obtained from a newspaper repository. The potential application of aspect-based multi-label techniques to classify textual data according to mood remains an open question. An exponential growth in potential label combinations presents a major hurdle in multi-label sentiment analysis. The objective of conventional multi-label learner (MLL) approaches is to enhance the precision and effectiveness of each multi-label learner through the utilization of label associations [7]. These methods can be categorized based on their approach to program development. Some techniques focus on testing various MLLs, assuming that the MLL can predict all labels and incorporate label correlations into the learning process [8; 9]. Other methods, such as EPS and RAKEL, rely on ensembles of single learners [10; 11]. This proposed work employs an ensemble learning approach, creating a group of learners capable of predicting a single label. These foundational learners are combined to create a multi-label classifier capable of predicting among all labels. The starting point for these learners is often the utilization of label associations with a single label. Figure 1 provides a visualization of the multi-labeling process. Sentiment analysis, a rapidly evolving field, has gained significant attention due to its relevance in various domains. Traditional approaches in MLL techniques have focused on developing individual MLLs, which may limit the generalization ability of a single learner [13]. To overcome this limitation, ensemble learning methods have surfaced, intending to enhance the overall capability of the learning system. This approach mitigates the likelihood of overfitting and fosters cross-learner creation within a single-label environment [13]. Ensemble learning involves building a cluster of basic MLLs that can collectively predict all connected labels, thereby significantly enhancing the generalization potential of the training approach [13]. This group of learners is referred to as an MLL ensemble [14]. The primary goal of multi-label-based ensemble learning is to develop a network of learners  
Figure 1: Virtualization of multi-labeling [12]. that is both accurate and diverse [15]. It is crucial because the base learners' collective abstraction capability and diversity are closely related to the ensemble's generalization flaw. Unlike conventional MLL ensemble methods that combine multiple base pair trainers into a single MLL, multi-label-driven ensemble methods utilize MLL as the foundation of the learning process. Despite their significance, multi-label supervised methods have received little attention in this context. Creating a collection of precise and diverse base solutions suitable for multi-label situations presents several challenges. One of the key research obstacles in this field involves the conventional ensemble learning methods, which mainly address challenges related to single-label learning.