Text Analytics and Blog/Forum Analysis

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If you have the correct qualifications and access to your own funding, either from your home country or your own finances, your application to work with this supervisor will be considered.

Project description

There are many documents that present author's subjective views on particular topics. Sentiment analysis is the extraction of attitudes and opinions from human-authored documents. The capture and analysis of such attitudes and opinions in an automated and structured fashion will offer a powerful technology to a number of problem domains, including business intelligence, marketing, national security, crime prevention and healthcare/wellbeing services. Blogs and forums in particular are an interesting and useful source for sentiment mining. Sentiment analysis can also be used to filter emails and other messages, or indicate abusive messages in newsgroups, or help users navigate via the Internet not only using topic keywords, but also opinions.

This project aims to develop technologies for extraction and analysis of sentiment and other relevant patterns from free text blogs and forums, using a combination of natural language processing, text mining and machine learning techniques. Research will include developing techniques for an automated analysis of the interactions between users within forum/blog communities (e.g. language used, roles adopted etc) and the kind of information that is exchanged. The work will involve building computational models of sentiment from which suitable templates for opinion extraction, prediction and integration will be designed.

The project can be placed in the context of different communities, including research (e.g. biomedical/bioinformatics forums), medical/health wellbeing or energy consumption.

The successful candidate will have an excellent first degree in Computer Science or Computational Linguistic, with clear interests and at least some relevant experience in text mining and language modelling. Understanding of machine learning will be a distinctive advantage.

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