Dynamic Social Media Information Extraction

Sentiment_Analysis_400_by_400This project will build on recent work concerning the extraction of information from Social Media such as Facebook and Twitter. It will develop methodology to provide a dynamic understanding of sentiments expressed on social media, including sentiments represented by emoticons, and to relate these to events such as news stories and stock market fluctuations. It will use techniques from time series and even spatio-temporal modelling to differentiate between long term underlying sentiments and ephemeral ones.   Results will be disseminated by means of an R package and also by a Shiny app that will, for example, provide a user who inputs a topic and a time frame with a detailed understanding of the changing nature of sentiments about that topic. The student would attend a course on scientific communication, such as the Communication Skills Course offered by the Royal Society. In this way, the student would make broad steps to becoming an effective scientific communicator.

Supervisor: Dr Julian Stander
Second Supervisor: Dr Luciana Dalla Valle