Shah, S.; Ghomeshi, H.; Vakaj, E.; Cooper, E.; Mohammad, R. An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis. Big Data Cogn. Comput.2023, 7, 85.
Shah, S.; Ghomeshi, H.; Vakaj, E.; Cooper, E.; Mohammad, R. An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis. Big Data Cogn. Comput. 2023, 7, 85.
Shah, S.; Ghomeshi, H.; Vakaj, E.; Cooper, E.; Mohammad, R. An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis. Big Data Cogn. Comput.2023, 7, 85.
Shah, S.; Ghomeshi, H.; Vakaj, E.; Cooper, E.; Mohammad, R. An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis. Big Data Cogn. Comput. 2023, 7, 85.
Abstract
Communication is a key method of expressing one's thoughts and opinions. Amongst many modalities, speech and writing are the most powerful and common forms of human communication. Analysing what and how people think has inherently been an interesting and progressive research domain. This includes bimodal sentiment analysis which is an emerging area in natural language processing (NLP) and has received a great deal of attention in recent years in a variety of areas including social opinion mining, health care, banking, and so on. At present, there are limited studies on bimodal conversational sentiment analysis as it proves to be a challenging area given the complex nature of the way humans express sentiment cues across various modalities. To address this gap, a comparison of the performance of multiple data modality models has been conducted on the MELD dataset, a widely-used dataset for benchmarking sentiment analysis within the research community. Our work then demonstrates the results of combining acoustic and linguistic representations. Lastly, our proposed neural network-based ensemble learning technique is employed over six transformer and deep learning-based models, achieving a State-Of-The-Art (SOTA) accuracy.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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