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Wednesday 14 November 12.30pm - 3.30pm

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Adaptive Opinion Mining and Intention Analysis from Text

Project Description

Social media analysis has gained significant attention in recent years, largely due to the success of online social networking platforms, and the consequent availability of a wealth of social network data. Such valuable data has in turn driven many useful applications such as branding, consumer perception and security monitoring.

This PhD project aims to tackle the problem of automatically identifying and classifying dynamically, socially produced online data into different sentiment and intent categories. To achieve this aim, you will be designing novel adaptive sentiment analysis and intention analysis algorithms to identify information of interest from noisy data channels. The outcome of this PhD project will enable users to derive knowledge from large amounts of online data and to formulate decisions, converting data into actionable knowledge.

The successful candidate should have (or expect to have) a UK Honours Degree (or equivalent) at 2.1 or above in Computer Science, Mathematics, Engineering, Data Science or related disciplines.

Knowledge: Good knowledge of Machine learning, statistical modelling and natural language processing. Some programming or software development experience would be beneficial.

This project is advertised in relation to the research areas in the discipline of Computing Science. Formal applications can be completed online: You should apply for PhD in Computing Science, to ensure that your application is passed to the correct College for processing. NOTE CLEARLY THE NAME OF THE SUPERVISOR and EXACT PROJECT TITLE ON THE APPLICATION FORM.

Informal inquiries can be made to Dr C Lin () with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Graduate School Admissions Unit ().

Funding Notes

There is no funding attached to this project, it is for self-funded students only


Zhao, Xin Wayne, et al. "We know what you want to buy: a demographic-based system for product recommendation on microblogs." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014.

Wang, Jinpeng, et al. "Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets." AAAI. 2015.

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