Funded by: NSF and ONR
PI: Carolyn Rose

LightSIDE is a free and open text mining toolbench, which is used both for teaching and for research. LightSIDE provides a convenient GUI environment for novice users of text classification technology easily run text extraction and classification experiments. On top of that, LightSIDE serves as a vehicle for dissemination of new techniques for effective application of machine learning to text mining, including novel feature extraction techniques (Gianfortoni et al., 2011). The newest version (LightSIDE 2.0) includes a model specification panel that enables easy use of multi-level modeling techniques from applied statistics as domain adaptation and multi-domain learning approaches. One of its most unique capabilities is its sophisticated support for error analysis.

You can download LightSIDE and find out more about the spinoff company, LightSIDE Labs, here.

This project provides an key enabling technology for research in the area of automated analysis of conversational interactions as well as analysis of the social aspects of text (i.e., perspective modeling, sentiment analysis, and opinion mining). We refer to work on these problems as social interpretation of language. Basic research contributions to the field of language technologies from my groupís work on these problems have been published in full and short papers at the fieldís top conferences including ACL, EACL, EMNLP, and SIGDIAL. Applications of this work to the field of education have been published in the top conferences in learning sciences including ICLS and CSCL as well as top conferences in educational technology including AIED and ITS as well as the top journal in Computer Supported Collaborative Learning, namely ijCSCL. You can find a complete list of publications from our work here.

You can read more about the findings from this strand of research here and impact here.

Carolyn Penstein Rose ( Carnegie Mellon University