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Detecting emotion based on multimodal sentiment analysis

   School of Computing

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  Dr Kia Dashtipour  No more applications being accepted  Self-Funded PhD Students Only

About the Project

Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative solution to computationally understand and harvest sentiments from videos by contextually exploiting audio, visual and textual cues.

This project aims to develop a novel multimodal based on natural language processing to detect the emotion from audio, visual and textual features.

Academic qualifications

A first degree (at least a 2.1) ideally in Computing Science with a good fundamental knowledge of Machine Learning, Mathematics, Deep Learning and Programming Languages.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other equivalent qualifications will be accepted. Full details of the University's policy are available online.

Essential attributes

  • Experience of fundamental Mathematics, Machines
  • Competent in one of the programming languages such as MATLAB, Python, C/C++
  • Knowledge of Mathematics, Machine Learning, Auto-visual, Natural Language processing
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

Funding Notes

This is a self-funded PhD project; applicants will be expected to pay their own fees


Huang, F., Li, X., Yuan, C., Zhang, S., Zhang, J. and Qiao, S., 2021. Attention-emotion-enhanced convolutional LSTM for sentiment analysis. IEEE Transactions on Neural Networks and Learning Systems.
Bandhakavi, A., Wiratunga, N. and Massie, S., 2021. Emotion‐aware polarity lexicons for Twitter sentiment analysis. Expert Systems, 38(7), p.e12332
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