Abstract
Since onomatopoeias are sensuous and ambiguous representation, information about these is often treated as multidimensional data. Recently, it has been analyzed based on visualization by mapping to a low dimensional space. In this study, we analyze the document data including onomatopoeias using self-organizing maps (SOM) that are widely used in the visualization of multivariate data. Further, the quantified information regarding onomatopoeia impression based on semantic orientations values is assigned to the map of the SOM. The effectiveness of the proposed analysis method was confirmed through visualization analysis for language resources on Twitter. The proposed analytical method can be useful as a tool to support the understanding of hidden linguistic properties of onomatopoeia.
