Multimodal Multilingual Sentiment Analysis

modalities are then fused to create a comprehensive view of sentiment, considering emotional and contextual aspects. Language agnostic approaches or multilingual embeddings ensure that the system can handle sentiments expressed in various languages. Machine learning or deep learning models are employed for sentiment analysis, which are trained, fine-tuned, and evaluated using appropriate metrics. The resulting system can be deployed for real-time sentiment analysis across a wide range of data, continuously improved through feedback and updates to adapt to evolving languages and sentiments.

The multimodal multilingual sentiment analysis combines the analysis of sentiments expressed through text, images, and audio, accounting for diverse languages. This comprehensive approach integrates techniques from natural language processing and computer vision, involving the fusion of modality-specific representations to understand and interpret sentiments accurately. The system is designed to be adaptable to multiple languages, making it a powerful tool for realtime sentiment analysis across various data sources, with ongoing refinements to enhance its effectiveness and applicability in our interconnected, multilingual world.

The primary objectives of this project encompass the development of an innovative real-time, context-aware system dedicated to detecting sentiment across multiple modalities, including text, audio, and video, thereby enhancing the scope of sentiment analysis in a multilingual context.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Computer Science

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:

Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.

Expert in one of these programming languages: C, Python or Matlab

Strong Communication Skills - Effective verbal and written communication abilities, including clarity, active listening, and articulation, essential for collaboration, client interaction, and conveying ideas effectively

Problem-Solving and Critical Thinking

Capability to analyse situations, identify problems, and propose effective solutions through logical and analytical thinking

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact k.dashtipour@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance

Application link: https://evision.napier.ac.uk/si/sits.urd/run/siw_sso.go?ElOlarlItFiG37xnH5PRRBvv3d563wLdwX4JfhYskMa3bJWTuc