Text and Web Analytics
Program Leader: Dr Guandong Xu
Researchers in this stream specialise in the use of data mining and statistical machine learning to analyse structured and unstructured data. We have specific expertise in text mining, social media mining, social network analysis, predictive analytics, smart business and recommender systems.
Our text analytics work is focused on extracting information from unstructured text to create structured data patterns. Our web analytics research is focused on collecting, analysing and reporting web data for the purpose of understanding and optimising web usage. This work provides new and exciting business insights into customer and online activities.
AAi is undertaking text and web analytics in the areas of:
Text Analytics
- Entity extraction, text categorization and text clustering
- Document summarisation
- Topic model and latent semantic analysis
- Topic discover and public event detection
- Search, retrieval and ranking
- Microblog and twitter mining
- Short text analysis and semantic enhancement
- Opinion mining and sentiment analysis
- Social spammer detection and social influence analysis
Web analytics
- Customer behaviour and access pattern mining
- Customer profiling and segmentation
- Customer retention and churn analysis
- Sales trend analysis and sales forecasting
- Marketing segmentation and cross-sale strategies
- Link analysis and link prediction
- User community detection and evolution
- Spatial-temporal analysis
Recommender systems
- Content-based, collaborative filtering, matrix factorisation algorithms
- Social recommender systems
- Cross-domain recommendation
- Location-based social networks, and point-of-interest recommendation
- Group-based recommendation
- Contextual-aware recommendation
- Mobile and handheld device-based recommendation systems