Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis

• Published in: Symmetry (Basel) 2023-03, Vol.15 (3), p.645
• Main Authors: Ahmed, Kanwal, Nadeem, Muhammad Imran, Li, Dun, Zheng, Zhiyun, Al-Kahtani, Nouf, Alkahtani, Hend Khalid, Mostafa, Samih M., Mamyrbayev, Orken
• Format: Article
• Language: English
• Subjects: Classification; Classifiers; Computational linguistics; Data mining; Deep learning; Dialects; Error analysis; Language; Language processing; Machine learning; meta-learning ensemble (MLE); Methods; Morphology; Natural language interfaces; Natural language processing; Sentiment analysis; Social networks; Support vector machines; Urdu language; Urdu sentiment analysis (USA); Word processing
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym15030645

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.