Ratings by category Ratings by category
Visualizing movie data
Henk Lamers
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Visualizing movie data
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Ratings by category
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As a next step, I find it interesting to see what our reviews are telling us when I show each category of a movie. I can imagine that the titles of the films are on the left. Suppose we start from the first 100 movies we have watched from the beginning of 2015? What does it look like? And what conclusions can we commit to? I’ll try programming this version slightly smarter than the earlier version.
143 Movie reviews of the 200 movies that we watched in 2015.
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Visualizing movie data
Judging movies Jeanne’s story
Henk’s story
I was about eight years old when each Sunday I received pocket money. It was not much, I believe it was ten cents. Like most children, we had to spend that money immediately and buy something. So first we plundered the local candy store and bought candy for five cents. In those days you would get a bag full of candy for that amount.
Before I turned eight years old I had only seen two films. The first film was an obligation of the primary school. It was a documentary about Martin Luther King. The second film was a Tarzan film that I did not like at all. Then the local cinema was demolished because it was not profitable. Then the only option was to looking forward to Wednesday every week. Because beside a house with a telephone there was also a house with a black and white TV in our street. All the streets children were welcome to watch children's programs at that TV on Wednesday-afternoon.
The other five cents were spend on the following. One of my brothers was a film operator in the local club house. Every week he showed films under the guidance of his teacher. The repertoire consisted of black and white films such as ‘Stan Laurel & Oliver Hardy’ or ‘Charlie Chaplin’. Easy movies for the children they could laugh about. Every Sunday afternoon we were away from home and our parents had time to rest. The phenomenon of ‘film’ was an important in our family. I can remember that my older brothers and sisters went to the cinema and that the film was extensively discussed afterwards. Was it a great movie? Based on a good story? Did the actresses and actors play well? They often went to films from ‘James Bond’ and also to ‘West-side Story’. When I grew up, I also visited the local cinemas a lot. It was in the family, in our DNA I think, my father was very good in telling stories and we just loved stories. Later, when I met Henk, we went to the cinema together: ‘Novecento’, ‘The Tree of Wooden Clogs’, ‘2001: A Space Odyssey’. We have seen a lot of films. Over the years when the colour TV became HD High Definition visuals and now even in 4K, four times the resolution, there is hardly any reason to go to the cinema. We now watch digital films and online, at times that suits us best. Every day a film, documentary or series so every day a story. It is instructive and opens your mind to new ideas, but it also keep you thinking about the world around you.
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In 1965 my parents bought a black and white TV. I actually watched everything that moved. In terms of content, it did not interest me very much. I watched Flipper, Lassie, Fury, Ivanhoe, Zorro, Batman, Ripcord, Spaceship Orion, Bonanza, The Avengers, Popeye, Skippy, The Flintstones, The Jetsons and The Thunderbirds. The worst thing to me was that before one of those series started you had to watch a deadly boring clock for a long time. A live broadcast of a clock on TV in black and white. After that an announcer appeared on screen who explained what was going to happen next. And then the misery started with series like the extremely annoying Peyton Place and the nerve-racking Coronation Street. Which of course always were broadcast when I wanted to see something else on the other TV-channel. We had two or three of them. That family fight lasted until I left the house when I was eighteen. Afterwards, Jeanne and I regularly went to the cinema and so grew my knowledge and the ability to judge films. But I did not always really ‘watch’ films. It was rather a kind of entertainment or a few hours evasion of reality. Of course I always had an opinion about every movie I saw. That is why we have set up this simple system that gives us the opportunity to give an opinion about a film in a reasonably ‘well-founded way’.
Ratings by category
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The films we saw in 2015, Locke, Mr. Turner, Mula sa kung ano ang noon and ‘71 received the highest possible score.
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Visualizing movie data
VMD_04_01
I started by creating a grid of numbers. There are 13 categories (13 columns) with decreasing numbers from top to bottom and from 10 to 0. The size of the display window is a bit of guesswork. I work on a size of 800 by 800 pixels. On the left side of the display window film titles have yet to be placed. And all 13 category labels should still come on top. I expect that I need much more space than 800 pixels in width and height. In the program I have added an empty draw block. Otherwise functions as keyReleased and timeStamp do not work. 6
Ratings by category
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VMD_04_02
To place the film titles, I created a text file with the first 100 titles that we have watched since 2015. Next, load this text file into Processing and show it in the display window. The order (from top to bottom) corresponds to the viewing order. The list starts with the film ‘Boyhood’, the first film we saw in 2015. The list ends with the film ‘Restless’. However, there is only one-third of the list visible in this graph, up to the film ‘Calvary’. And that is film number 38. Putting another 62 films in this display height makes no sense because the point size would become too small to read. 7
Visualizing movie data
VMD_04_03
To get all the movies titles on the left in the graph, I have a few options. Reduce the line spacing, reduce the font size or I can increase the size of the display window. I choose to use all three possibilities and I end up with 1500 x 1300 pixels. I also added the names of the categories on the top of the page.
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Ratings by category
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VMD_04_04
Another stage where I further optimize the distances. The category names (the labels of the columns) are still too far from the category columns. I’m going to put them closer and place them on an angle of 45º. The category numbers are now placed on an imaginary square. The display window is now 1460 x 1228 pixels. And the grid is built with squares of 90 x 90 pixels. Testing a first line which is drawn through the numbers who rated the film ‘Boyhood’. That does not look good. The lines are too stiff. It should be more fluid.
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Visualizing movie data
VMD_04_05
In order to make more fluid lines I tried the curveVertex function. The problem here is that the curveVertex function uses Catmull-Rom splines. It doesn’t make beautiful curves though. In the end I opted for bezier curves. For the best quality curve that is the best solution, but it requires more passes of data to describe the curve. Four anchor points and four control points per line. That means 13 x 8 points per bezier curve. That is 104 numbers for the first movie. Thus, in total there must be 10.400 points calculated to make the final visualization. 10
Ratings by category
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VMD_04_06
The first six films drawn using bezier curves.
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Visualizing movie data
VMD_04_07
I have now drawn 26 films with bezier curves. And it shows directly the weakness of this visualization method. Since all lines have the same colour and thickness it is difficult to see which movie has scored which number in which category. At a later stage I will try to solve this issue but the problem is not completely solvable.
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Ratings by category
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VMD_04_08
Here I am about half way with the positioning of bezier curves. I placed the curves in a very straightforward way. I know that this can be done with more intelligence but I will not have time enough to solve this problem now. I think it requires an additional study which I might do in a later stage.
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Visualizing movie data
VMD_04_09
In this sample I am about to place a fourth number of bezier curves.
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Ratings by category
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VMD_04_10
All films are now using a line to go through the ratings of the categories.
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Visualizing movie data
VMD_04_11
With all the lines in their place, it is now the time to bring in the Futura font. I have changed the background colour to black and the font colour is white. The colour of the lines is gray with 50% transparency.
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Ratings by category
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VMD_04_12
Time for a number of tests with line widths. Some are absolutely exaggerated. Others are functional. These variations also show that the number columns have to be written as a last item. Otherwise they will be overwritten by the bezier lines. And I shifted the column with movie titles slightly to create some space between the start of the bezier lines and the end of the movie titles.
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Visualizing movie data
VMD_04_13
This visualization shows that most of our ratings are located between 5 and 8.
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Ratings by category
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VMD_04_14
A more dense visualization, but not as clear as the previous one.
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Visualizing movie data
VMD_04_15
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Ratings by category
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VMD_04_16
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Visualizing movie data
VMD_04_17
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Ratings by category
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VMD_04_18
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Visualizing movie data
VMD_04_19
Here I am trying to solve a problem that popped up in VMD_04_07. To what extent is it possible to get more distinction between the bezier curves themselves. I start with two colours: red and green. This results in a strange effect. When a certain amount of red and green lines overlap it creates an additional colour. It looks orange-like. At least that seems to be orange but if you make the lines thicker (sample on the right page) it seems to be some light version of something brown-ish.
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Ratings by category
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VMD_04_20
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Visualizing movie data
VMD_04_21
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Ratings by category
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VMD_04_22
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Visualizing movie data
VMD_04_23
I added a blue colour. Now it seems that there are many more shades of additional colour variations possible.
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VMD_04_24
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Visualizing movie data
VMD_04_25
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Ratings by category
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VMD_04_26
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Visualizing movie data
VMD_04_27
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VMD_04_28
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Visualizing movie data
VMD_04_29
What happens when I make an ascending colour scale from 0 to 360? I switch to colour mode HSB. HSB is easier to work with (as a human).
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Ratings by category
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VMD_04_30
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Visualizing movie data
VMD_04_31
All movies that have been honoured with at least once the highest possible value of 10 points.
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Ratings by category
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VMD_04_32
All movies that have been awarded with at least once the highest value of 9 points or higher.
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Visualizing movie data
VMD_04_33
And finally: which movies have been rewarded with at least once the highest value of 8 points or more.
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Ratings by category
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VMD_04_34
A quick conclusion. I am tempting to say that if a movie did not score an 8, 9 or 10 in the assessment it would be not a good movie. That means it is of a lower level than films who scored at least one 8. Or one 9. Or one 10. This visualization is showing the worst movies of all 100 movies we have seen since the beginning of 2015. In total only 27 movies. So a little over a quarter. That means that three-quarters of the 100 films that we have seen always had something of good quality in them. And that’s very reassuring for filmmakers, the film industry and for us. 39
Visualizing movie data
The films that received ten points in 2015 have the following similarities: - A good story is the foundation for a film. - Originality is not always a requirement. - Other concepts than the usual ones are appreciated. - The camera work is very important. - The length of a film is not important. - Is it too short? Then it is usually good. - Is it loo long? Then it is usually wrong. - A high budget for a film is no guarantee for quality.
Results can be found here: Flickr Text and design: Henk Lamers and Jeanne de Bont Copyright Š 2016 Loftmatic, The Netherlands. All rights reserved
143 Movie reviews of the 200 movies that we watched in 2015.
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Ratings by category
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