In our tag clouds, as explored in Figures 4 and 5, input terms are chosen as the nearest neighbor word for each scene. In these visualizations we have the following simultaneous properties:
- Selected words, that most appropriately characterize a scene.

- The selected words accompanying the sequence of scenes, in scene order. - Quality of the fit between characterizing word and scene.

- All pairwise relationships of words and scenes taken into consideration. Properties such as these, seen in the display, can be used to distinguish one episode (program) from another.

One of the most appealing aspects of our approach is that all phases of the processing are automated, and there are no user-set parameters or other user intervention required.

We checked that the pertinent words found to characterize each scene were unique. This was always the case. If the same word were found to be closest to two scenes we could of course use any such multiple occurrences of words.

The motivation for our tag clouds is to go beyond frequency of occurrence statistics and instead visualize at once multiple facets of the filmscripts.

## 3.2 Tracking Of Characters

In the sequence of scenes balance must be maintained as well as tempo-related contrast. In such areas as contrasts between interior and exterior scenes, day and night, and the presence or absence of principal and secondary characters, the filmscript must reflect vital aids and hints to the viewer, provoking both continuity of understanding by the viewer and discontinuity to trigger hightened attention.

We will look at the principal characters in the CSI scripts and television series programs: Gil Grissom, Warrick Brown, Nick Stokes, Catherine Willows, Jim Brass, and Sara Sidle. We will refer to them by the first or family name mainly used: Grissom, Warrick, Nick, Catherine, Brass and Sara.

The Correspondence Analysis allows us to easily seek the principal character who is closest to each scene. In the plot of scenes crossed by all words used in the filmscript, which naturally contains the character names, we look for proximity - in the full dimensional Euclidean, factor space, so no approximation is involved - between the character and the scenes. The relative importance is expressed by size in Figures 6 and 7. This relative importance is a scaled version of the log (base 10) of the squared Euclidean distance. (Using the distance or squared distance, and taking the log, there is clearly no effect on monotonicity of proximity. We take the log for improved visual appearance.)
We can see at a glance how Grissom pervades these films; whether characters reappear as the most crucial players implying intertwining of different actors; how the central roles of male and female characters alternate; and so on.