We use cookies to give you the best experience possible. By continuing we’ll assume you’re on board with our cookie policy

To study the drama interspersed in the screenplay, the movie is treated as a collection of scenes. The scenes are identified as episodes or segments with a continuity of situation or location. Another way to study the movie would be to divide it into uniform time segments. But this method does not help preserve the continuity in drama and interferes in the process of documenting it through its logical span. It is more important to maintain a logical unit of the story and its emotions rather than a fractional physical unit of measure.

My Fair Lady was divided into 16 explicit scenes for this study. Each scene in the movie was rated on the basis of its dramatic intensity (DI) on an integer scale of 1 to 10. The dramatic intensity parameter of a scene used here can be defined as the level of drama psychologically experienced by a viewer while watching a particular scene. Though the DI parameter is subjective, it is considered to be a good measure of the scene wise response of an average movie viewer to a movie plot.


Thus when summed up it can indicate the overall reaction of the viewer to the movie as engaging or non-engaging. A rounded average of ratings by two viewers was used to determine the score for each scene. Besides the tabulation of scenes in the movie and the corresponding DI scores, the table also has data for level of surprise and approx. runtime of the scenes listed. Though these two parameters have not been used in the analysis that follows in the next section, they are of potential theoretical interest, especially in the extension of the proposition space of this paper.

The level of surprise score values for this movie are very similar to the DI scores, but this is not necessary in all cases. (For example a scene may surprise the viewer by logical twists without any dramatic play. ) There are some interesting observations that can be made from the table above. Long dramatically intense scenes are interspersed with dull scenes (with scores below a mid-score of 5). It can be conjectured that these relatively dull scenes are used to deliver insipid details that are not intriguing yet paramount to the plot, besides providing, relief from sustained high intensity viewing.

The movie also has a relative quick change of scenes in the beginning and end of the movie that can be considered analogous to the introduction and wrapping up of a plot. Construing the Dramatic Intensity scores The evidence of this study suggests that movies can be identified as interesting on the basis of the Dramatic Intensity Distribution graphs. The graph of an interesting movie will tend to be skewed towards the left, since it will have fewer number of low intensity dramatic scenes.

Of course this will not apply to movies of a genre where drama is understated. In such cases the parameter will have to be aptly determined. The dramatic intensity level is however a convenient parameter of measure that can be applied to most popular movies. Another important implication of this graph is the possible shape of graphs of movies that are not interesting. Movies, which are not interesting will have more scenes with lower DI scores. Thus the graph will be skewed towards the right, with a fewer number of scenes with high dramatic intensity scores.

The graph is not an absolute determinant, as there may be cases where there are a number of scenes that have a short runtime but have extremely high DI scores. And most of the movie constitutes long scenes with low or average DI scores. In such cases the movie may not be very interesting on the whole. The run-time of scenes has to be taken into considerations to determine the actual quotient of interesting scenes in the movie. An interesting extension of this study would be to collect the graphs of a large sample set of popular, interesting and successful movies and compare their shapes.

There may be an interesting insight to a possible threshold level of skew or pattern to the graph of above average movies. It would also be interesting to compare multiple parameters for different movies to explore their interrelationships. To sum up, this study shows that a combination of economic and data analysis theories can be combined to discover common patterns and traits in successful Hollywood and even world cinema. While some of these models can be region specific, they also can be built with universal parameters.

These models can contribute to the understandings and studies of scholars of cinema with respect to contemporary and classic trends and the evolving choices of the average viewer. Alteration of parameters from the basic can be applied to study different niche crowd. The industry can also use such models to explore potential projects.


Crawford, W. (1990, October 29). Trends in Hollywood Cinema: Is cinema using ideas or merely aping trends? The Washington Post, p. A3. Clarkson, P. M. , & Lloyd, E. S. (1990).

Analytical measurement: Processes of learning and invention in the evolution and development of mathematical concepts. In S. T. Walker & K. R. Foquel (Eds. ), Mathematical analysis in economic trends: Comparative developmental perspectives (pp. 540-578). Cambridge: Cambridge UniversityPress. Doumpous, M. , C. Zopounidis. (2002, August 31). Multicriteria Decision Aid Classification Methods (Applied Optimization). Springer. Eckholm, E. (1985, June 25). Studying Cinema: Entertainment science. The New York Times, pp. C1, C3. Gibbons, A. (1991). Tools for analyzing probability data: Skews and Kurtosis, 251, 1561-1562.

Share this Post!

Send a Comment

Your email address will not be published.