Overview
Topic status: We're looking for students to study this topic.
Textual data in the world can be roughly categorized into two main types: facts and opinions. Much effort has been devoted to fact-based information processing in the past decades and many useful techniques have been developed for information retrieval or text mining. In recent years opinion-based information processing has also been receiving increasingly more attention from researchers. Understanding people's opinions about some subject matters or issues is important for organizational decision making in general.
For instance, organizations are keen on retrieving and analysing customers' opinions about products and services so as to develop more effective business strategies for product design and customer-centric marketing. Nevertheless, identifying opinion sources, extracting prominent topic features, summarizing relevant opinions and effectively predicting the polarity of an opinion are all very challenging tasks.
This proposed PhD project aims to develop an innovative methodology for automatic access to public opinion via analysing free textual information existing online. Text mining, Natural Language Processing and Machine Learning techniques will be deployed in the work to develop a collection of methods and algorithms to discover public opinions from text and measure their polarity. The outcome of this research will have theoretical contributions to opinion mining and sentiment analysis, and applicable contributions to governments and commercial corporate by helping efficiently access public opinions against a project for which the agencies may never have sufficient time and resources to complete. Hardworking, self-motivated, capable of working under stress and willing to take challenges, applicants from Information Technology, Information Sciences and Computer Science backgrounds, with Distinction-level academic results and/or research experience, are expected to join us in this project. Any enquiries are welcome.
This project is part of the Smart Services CRC and has the option to apply for scholarship.
- Study level
- PhD
- Supervisors
- QUT
- Organisational unit
Science and Engineering Faculty
- Research area
- Contact
- Please contact the supervisor.
Associate Professor Dian Tjondronegoro
