Text data analytics
Textual data such as tweets and news is abundant on the web. However, extracting useful information from such a deluge of data is hardly possible for a human. In this research we study automated text analysis methods based on sparse optimization. In particular, we use sparse PCA and Elastic Net regression for extracting intelligible topics from a big textual corpus and for obtaining time-based signals quantifying the strength of each topic in time. These signals can then be used as regressors for modeling or predicting other related numerical indices.
ERC Sector:
- PE7_1 Control engineering
- PE6_11 Machine learning, statistical data processing and applications using signal processing
Keywords:
- Machine learning
- Text analysis
- Topic modeling
- Data analytics
- Latent Dirichelet Allocation
- Principal Component Analysis
Research groups
Contact
- CALAFIORE GIUSEPPE CARLO - Manager
