The color scheme is subjective. I originally interpreted the colors in terms of temperature, the red tint refers to the ‘hotness’ of the comment with the deepest saturation being the most dugg comment. I can see the blue being a positive metric but i may change the color scheme to something more standard like a green to red spectrum.

One of my basic assumptions were that people who wrote a fair amount of words on a comment have a tendency to get the most feedback. A pie chart was used to show the percentage of words typed as compared to the entire group. The color of each slice refers to the level of agreement the comment has generated and the radius refers to how many replies your comment has made. In theory, the more prominent the slice, the more meaningful it was to all readers. In reality, I’ve noticed that most users prefer not to respond/rate long-worded comments (unless it’s ascii art related) but to simple one liners.

The scaling problem was actually accidentally intentional (if that makes any sense). Since the comments were rated, rotating the diagram around allowed you to focus more on positive or negative comments without completely warping the results. My only regret was that I didn’t properly describe it.

My method for creating this chart was a hit or miss approach. Mixing and matching these different metrics with different attributes of a pie chart proved to be more challenging than I thought however I’m pleased with the overall result.

The color scheme is subjective. I originally interpreted the colors in terms of temperature, the red tint refers to the ‘hotness’ of the comment with the deepest saturation being the most dugg comment. I can see the blue being a positive metric but i may change the color scheme to something more standard like a green to red spectrum.

One of my basic assumptions were that people who wrote a fair amount of words on a comment have a tendency to get the most feedback. A pie chart was used to show the percentage of words typed as compared to the entire group. The color of each slice refers to the level of agreement the comment has generated and the radius refers to how many replies your comment has made. In theory, the more prominent the slice, the more meaningful it was to all readers. In reality, I’ve noticed that most users prefer not to respond/rate long-worded comments (unless it’s ascii art related) but to simple one liners.

The scaling problem was actually accidentally intentional (if that makes any sense). Since the comments were rated, rotating the diagram around allowed you to focus more on positive or negative comments without completely warping the results. My only regret was that I didn’t properly describe it.

My method for creating this chart was a hit or miss approach. Mixing and matching these different metrics with different attributes of a pie chart proved to be more challenging than I thought however I’m pleased with the overall result.

• A color scheme from red to blue would be interpreted by many people as ranging from negative (red) to positive (blue) – in this case it’s the other way around. But as we know color is a difficult attribute to show a rating as the symbolism of colors vary for different target audiences. Maybe therefore the rating is also indicated by the position on the z-axis.
• Labeling the axis would make it easier to read the attributes of a conversation.
• A wedge represents the % of words in regard to the total count of words of all comments.
• 3D representation is often used to indicate another attribute that could not be displayed in plain 2D. In this case though the third dimention is used to duplicate the color-indication if I understand this correctly.

I’ll check whether Manuel could clarify what question this visualization adresses.

A color scheme from red to blue would be interpreted by many people as ranging from negative (red) to positive (blue) – in this case it’s the other way around. But as we know color is a difficult attribute to show a rating as the symbolism of colors vary for different target audiences. Maybe therefore the rating is also indicated by the position on the z-axis.
Labeling the axis would make it easier to read the attributes of a conversation.
A wedge represents the % of words in regard to the total count of words of all comments.
3D representation is often used to indicate another attribute that could not be displayed in plain 2D. In this case though the third dimention is used to duplicate the color-indication if I understand this correctly.

I’ll check whether Manuel could clarify what question this visualization adresses.

Regarding the usage of a three dimensional Pie Chart, it is an interesting question. I have trouble understanding what I am looking at in the visualization. I’m assuming blue is positive rating and red is negative rating; however, blue has negative numbers and red positive numbers. It must be the other way around.

I’m very interested in techniques that try to visualize unstructured text. I hope I’m understanding what is represented. Each wedge represents the % of words in a particular comment (or a comment and the replies to the comment) as part of a whole discussion. The length of the wedge gives an indication of the volume of replies. Lastly, there is a vertical component that shows rating.

If that is true, than the widest, longest, highest wedge would seemingly be where most of the highly-rated, active discussion is occurring.

The 3-d representation always will have a scaling problem when viewed in 2-d. Additionally, there are 3 dimensions, but just one text label. It is not totally clear what that number represents. I think it is the positive/negative quality. As I looked at the visualization though, I kept thinking it should represent the length of the wedges.

For me, I’m not positive my understanding of the discussion is enhanced by the visual. It would be interesting to know what question the visualization is addressing. What would someone do with this information?

It is an interesting project. Visualizing text is still in its infancy. All experimentation will advance best practices.

Thanks for bringing this to our attention. Sorry for my rambling comment.

Regarding the usage of a three dimensional Pie Chart, it is an interesting question. I have trouble understanding what I am looking at in the visualization. I’m assuming blue is positive rating and red is negative rating; however, blue has negative numbers and red positive numbers. It must be the other way around.

I’m very interested in techniques that try to visualize unstructured text. I hope I’m understanding what is represented. Each wedge represents the % of words in a particular comment (or a comment and the replies to the comment) as part of a whole discussion. The length of the wedge gives an indication of the volume of replies. Lastly, there is a vertical component that shows rating.

If that is true, than the widest, longest, highest wedge would seemingly be where most of the highly-rated, active discussion is occurring.

The 3-d representation always will have a scaling problem when viewed in 2-d. Additionally, there are 3 dimensions, but just one text label. It is not totally clear what that number represents. I think it is the positive/negative quality. As I looked at the visualization though, I kept thinking it should represent the length of the wedges.

For me, I’m not positive my understanding of the discussion is enhanced by the visual. It would be interesting to know what question the visualization is addressing. What would someone do with this information?

It is an interesting project. Visualizing text is still in its infancy. All experimentation will advance best practices.

Thanks for bringing this to our attention. Sorry for my rambling comment.