# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
###### Abstract  
This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA). aspect-based sentiment analysis; meta-weigher; imbalance data +
Footnote †: journal:  
processing, and artificial intelligence. The aspect is the focus of the opinionated polarity, which may be explicitly mentioned or implicitly indicated through a set of indicators in the text. The explicit aspect, also known as a target, appears explicitly in the sentence, while the implicit aspect, also known as a category, is mentioned in the text implicitly through its indicators. In the literature, the former is known as aspect-target sentiment analysis (ATSA), and the latter is called aspect-category sentiment analysis (ACSA). Given a sentence review, ABSA first extracts a set of aspects and then identifies their respective polarities. MABSA is mainly performed through three steps: detection, classification, and aggregation. The detection step requires extracting the evaluated aspects in the text; the classification step classifies the sentiment into a predefined set of polarities, e.g., positive, negative, and neutral, in response to the extracted aspects; and the aggregation step produces a concise summary [6]. However, ATE and ASC components are considered two-step pipelined tasks, and SE is utilized separately. These three smaller components created for multi-task learning (MTL) have been combined to form an end-to-end model. Utilizing MTL can concurrently attain aspects of the opinions and sentiment polarities with a shared encoder. Learning about each subtask can save a certain amount of computing resources and enhance speed [7]. However, compared to single-task learning systems, MTL-based MABSA confronts more difficulties. The first issue is that different annotated subtasks may source an imbalanced label classification. In an MTL scenario, it is challenging to create enough examples with an equivalent number of data labels for each subtask. This issue is made more explicit in a sequence marking task when the class \"others\" makes up the majority of the tag sets. A subtask label occurrence might not be enough to train a robust neural network. Taking an applied discharge summary corpus in this research as an instance, the number of examples with the most commonly labeled data is compared against the unlabeled data. With a small dataset, very unbalanced data may mean that some classes do not provide enough support for an MTL neural network to perform as intended [8].