# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
## 8 Conclusions  
In conclusion, this research proposes a multi-task approach for aspect-based sentiment analysis, utilizing deep neural network models for sentiment detection. To address the issue of insufficient and unbalanced data in the MABSA task, the SMOTE-OSS-CNN, and meta-based self-training approach MMS are suggested. MMS contains the meta-weightier, the student model, and the instructor model, and a 3-step meta-updating technique is suggested to avoid noise in the automatically labeled data. Overall, this study has implications for discharge summary sentiment analysis. Future work will involve dealing with numerous sequence labeling jobs under more challenging low-data conditions. Moreover, this paper intends to explore data augmentation to address imbalanced data to increase performance stance detection. Additionally, feature extraction is required for greater contextual information,  
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] GP-GCN: Global features of orthogonal projection and local dependency fused graph convolutional networks for aspect-level sentiment classification. _Connect. Sci._**2022**, _34_, 1785-1806. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model. _Appl. Artif. Intell._**2022**, _36_, 2123094. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Cross-Modal Multitask Transformer for End-to-End Multimodal Aspect-Based Sentiment Analysis. _Inf. Process. Manag._**2022**, _59_, 103038. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] [PERSON] Implementation of specialised attention mechanisms: ICD-10 classification of Gastrointestinal discharge summaries in English, Spanish and Swedish. _J. Biomed. Inform._**2022**, _130_, 104050. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Explainable ICD multi-label classification of EHRs in Spanish with convolutional attention. _Int. J. Med. Inform._**2022**, _157_, 104615. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Pseudo Label-wise Attention Network for Automatic ICD Coding. _IEEE J. Biomed. Health Inform._**2022**, _26_, 5201-5212. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] GraphXCOVID: Explainable deep graph diffusion pseudo-labelling for identifying COVID-19 on chest X-rays. _Pattern Recognit._**2022**, _122_, 108274. [CrossRef] [PubMed]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] A multi-objective/multi-task learning framework induced by Pareto stationarity. In Proceedings of the International Conference on Machine Learning, Baltimore, MD, USA, 17-23 July 2022; pp. 15895-15907. * [PERSON] and [PERSON] (2022) [PERSON]; [PERSON] Co-attentive multi-task convolutional neural network for facial expression recognition. _Pattern Recognit._**2022**, _123_, 108401. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A survey of multi-task learning in natural language processing: Regarding task relatedness and training methods. _arXiv_**2022**, arXiv:2204.03508. * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON] Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings. _arXiv_**2022**, arXiv:2209.07494. * [PERSON] and [PERSON] (2023) [PERSON]; [PERSON] Bridging towers of multi-task learning with a gating mechanism for aspect-based sentiment analysis and sequential metaphor identification. In Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA, 7-14 February 2023; pp. 13534-13542. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON] End-to-end aspect-based sentiment analysis with hierarchical multi-task learning. _Neurocomputing_**2021**, _455_, 178-188. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Relation-aware collaborative learning for unified aspect-based sentiment analysis.