An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis

Shariq Shah*, Hossein Ghomeshi, Edlira Vakaj, Emmett Cooper, Rasheed Mohammad

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    5 Citations (SciVal)


    Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.
    Original languageEnglish
    Article number85
    JournalBig Data and Cognitive Computing
    Issue number2
    Publication statusPublished (VoR) - 30 Apr 2023


    This paper has been produced as part of the broader UK Research and Innovation (UKRI)-sponsored Knowledge Transfer Partnership project between FourNet and Birmingham City University. The research has received funding from UKRI under project reference number 512001.

    FundersFunder number
    broader UK Research and Innovation
    Birmingham City University512001
    UK Research and Innovation


      • ensemble learning; bimodal; sentiment analysis; neural network; transformer


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