Emotion Detection from Social Media Text
PhDs and postgraduate research
This project is now closed. The details below are for information purposes only.
This is a self-funded, 3 year full-time or 6 year part-time PhD studentship, to commence in February 2019 or October 2019. The project is supervised by Dr Mihaela Cocea.
Once thought to be separate from human cognition, emotions are now recognised as a key factor in human judgement, attitude and behaviour. With the rapid growth of social media, the recognition of emotions from text - such as posts on Facebook or Twitter -- poses several research challenges and can help in tackling problems relevant to wider society, in areas such as health and security.
You'll be harnessing machine learning to automatically identify the emotion underpinning social media post, detecting the intent behind these messages.
The work will include:
- investigating new approaches to feature selection, as well as transparent and interpretable learning techniques, making outputs easier to visualise and interpret
- working as part of our Computational Intelligence Research Group
- developing your skills in data analysis (including data pre-processing, dimensionality reduction and feature selection), machine learning in general, as well as skills related to working with textual data
Fees and funding
PhD full-time and part-time courses are eligible for the Government Doctoral Loan
Home/EU/CI full-time students: £4,260 p/a*
Home/EU/CI part-time students: £2,130 p/a*
International full-time students: £15,100 p/a*
International part-time students: £7,550 p/a*
Bench fees may also apply - for more information please contact the project supervisor [LINK to 'How to Apply' section] .
By Publication Fees 2018/2019
Members of staff: £1,550 p/a*
External candidates: £4,260 p/a*
*All fees are subject to annual increase.
A good honours degree or equivalent in a relevant subject or a master’s degree in an appropriate subject.
Exceptionally, equivalent professional experience and/or qualifications will be considered. All applicants are subject to interview.
English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
The candidate should have a background in Computer Science or Mathematics. Prior knowledge of machine learning is desirable.