Tag Clouds for Displaying Semantics: The Case of Filmscripts

TL;DR

This paper introduces an enhanced tag cloud visualization method that captures semantic relationships in filmscripts by incorporating context and word pertinence, aiding analysis of structured, time-ordered texts.

Contribution

It presents a novel tag cloud visualization integrating semantic context and word relevance, specifically applied to filmscript analysis for improved plot characterization.

Findings

Enhanced visualization captures semantic relationships effectively.

Incorporates context and word pertinence beyond frequency.

Facilitates analysis of structured, time-ordered texts.

Abstract

We relate tag clouds to other forms of visualization, including planar or reduced dimensionality mapping, and Kohonen self-organizing maps. Using a modified tag cloud visualization, we incorporate other information into it, including text sequence and most pertinent words. Our notion of word pertinence goes beyond just word frequency and instead takes a word in a mathematical sense as located at the average of all of its pairwise relationships. We capture semantics through context, taken as all pairwise relationships. Our domain of application is that of filmscript analysis. The analysis of filmscripts, always important for cinema, is experiencing a major gain in importance in the context of television. Our objective in this work is to visualize the semantics of filmscript, and beyond filmscript any other partially structured, time-ordered, sequence of text segments. In particular we develop an innovative approach to plot characterization.