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

Gruppi di ricerca

Persone di riferimento