Sentiment detection determines whether discourse is subjective, and if so, its polarity, intensity and usefulness. Automatic sentiment detection is essential for discriminating fact from opinion in information gathering applications, and extracting prevalent opinions about items. We propose to develop computational mechanisms that detect sentiment from texts, and summarize the sentiment expressed in multiple texts. The developed techniques will be applied to textual feedback provided by customers and employees about different aspects of service industries. The implemented computer system will detect the sentiment expressed in this feedback and produce summaries, which will be used to make recommendations for service improvements.
|Effective start/end date||19/12/08 → 31/12/12|
- Australian Research Council (ARC): AUD217,750.00
- Australian Research Council (ARC): AUD76,880.00
- GAPbuster Worldwide: AUD90,518.00
- Monash University
- Australian Research Council (ARC)