Visualizing common sense connections with Luminoso

Authors:
Robert Speer;Catherine Havasi;Nichole Treadway;Henry Lieberman
Affiliations:
Massachusettes Institute of Technology, Cambridge, MA, USA;Massachusettes Institute of Technology, Cambridge, MA, USA;Massachusettes Institute of Technology, Cambridge, MA, USA;Massachusettes Institute of Technology, Cambridge, MA, USA
Venue:
Proceedings of the first international workshop on Intelligent visual interfaces for text analysis
Year:
2010

Citing 7
Cited 0

Guiding knowledge discovery through interactive data mining

Managing data mining technologies in organizations
Spiders: a new user interface for rotation and visualization of n-dimensional point sets

VIS '94 Proceedings of the conference on Visualization '94
IN-SPIRE InfoVis 2004 Contest Entry

INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Interactive and Dynamic Graphics for Data Analysis With R and GGobi

Interactive and Dynamic Graphics for Data Analysis With R and GGobi
Digital Intuition: Applying Common Sense Using Dimensionality Reduction

IEEE Intelligent Systems
AnalogySpace: reducing the dimensionality of common sense knowledge

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Discovering semantic relations using singular value decomposition based techniques

Discovering semantic relations using singular value decomposition based techniques

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present Luminoso, a tool that helps researchers to visualize and understand a dimensionality-reduced semantic space based on textual information by exploring it interactively. It streamlines the process of creating such a space by taking input from a directory of text documents, and optionally including common-sense background information. This interface is useful for interactively discovering trends in a text corpus, such as free-text responses to a survey. We discuss a case study about restaurant reviews to show how Luminoso can be used for opinion mining.