emotion is based on particular transitions. So this establishes well the possibility that words and phrases are not taken literally but instead can appropriately capture and represent such transition. Text, says McKee, is the "sensory surface" of a work of art (counterposing it to the subtext, or underlying emotion or perception).

Simple words can express complex underlying reality. Aristotle, for example, used words in common usage to express technically loaded concepts ([18], p.

169), and Freud did also.

Best practice in film script writing includes the following. Present tense dominates all ([15], p. 395): "The ontology of the screen is *an absolute present* tense in constant vivid movement" (emphasis in original). Clipped diction is needed. Generic nouns are avoided in favor of specific terms, and similarly adjectives and adverbs are to be avoided. The verb "to be" is to be avoided because: "Onscreen nothing is in a state of being; story life is an unending flux of change, of becoming" (p. 396). Simile and metaphor are out, as is any explicit positioning of context on behalf of the reader of the script or viewer of the film.

## 4 Methodology: Euclidean Embedding Through Correspondence Analysis, And Clustering The Succession Of Film Script Scenes

We have already noted (section 1) some novel aspects of our methodology. We begin with the display of data (e.g., scenes and/or words) where visualization of relationships is greatly facilitated by having a Euclidean embedding. We show how Correspondence Analysis furnishes such a metric space embedding of the information present in the film script text, and furthermore how this facilitates an ultrametric (i.e. hierarchical) embedding that takes account of the temporal, semantic dynamic of the film script narrative.

## 4.1 A Note On Correspondence Analysis

Correspondence Analysis [18] takes input data in the form of frequencies of occurrence, or counts, and other forms of data, and produces such a Euclidean embedding. The Appendix provides a short introduction to Correspondence Analysis and hierarchical clustering.

We start with a cross-tabulation of a set of observations and a set of attributes. This starting point is an array of counts of presence versus absence, or frequency of occurrence. From this input data, we can embed the observations and attributes in a Euclidean space. This factor space is mathematically optimal in a certain sense (using the least squares criterion, which is also Huyghens' principle of decomposition of inertia). Furthermore a Euclidean space allows for easy visualization that would be more awkward to arrange otherwise.