- Great. So you may notice something interesting
about this introductory slide,
my name is Sam Sohn, I'm from Rutgers University,
and I will be presenting this paper on behalf of Tugba,
the first author, who unfortunately cannot be here
due to a medical problem.
This introduction though, was actually written for me
in a script that the authors have provided,
so they've taken the proper precautions to ensure
that I don't forget my name
and that I don't miss any details to convey,
so lets get started.
Okay, um...
Word clouds appeal to people of all ages and occupations.
For analytics, they have several well-known drawbacks,
especially the relationship between size and salience.
However, studies show that the most common use
of word clouds are in fact for non-analytic purposes,
such as preparing a gift, introducing a topic to students,
or just playful exploration.
Some example comments provided by users in a study include
"Wordles have more emotional emphasis, colors and layouts
to enhance the meaning."
And "wordles are colorful, more visually interesting,
more of an emotional response and connection
with the viewer than the tag clouds."
Despite this important role, most word cloud tools
don't pay much attention to font and color choices.
They often just use the same font and color pallettes
for all clouds, or they pick them at random.
The study found, also, that the wordle tool
is more successful than others because of its tight layouts,
and the font and color options it provides.
Here, we see two different word clouds about climate change,
and we can see that the fonts and colors
have a very different emotional impact.
The overall goal of our paper is to automatically
choose fonts and colors in an intelligent way,
based specifically on emotional impact.
Our first sub-goal is to obtain font scores
for a set of eight affects.
We use the same affect set that is used in a previous study
Bartram et al., so that we can study
their color pallette data set
and connect our results to theirs.
The set of eight affects consists of four core affects:
calm, exciting, positive and negative;
and four complex affects:
playful, serious, disturbing and trustworthy.
For the font set, we use a data set from O'Donovan et al..
Our second sub-goal is to understand the impact
of fonts and color pallettes on word clouds
by conducting user studies.
In the images on this slide,
we can see sample word clouds that are found to be
congruent w-- congruent for each affect.
For example, under calm we have both font and color choices
that users consider to be calm.
These are quite different from the ones for exciting.
Our first sub-goal is to obtain font affect scores
for the eight affects.
We rely on several different methods for this.
The seed data set that we use provides human ratings
for different attributes on a small set of fonts.
Most of these attributes are visual in nature,
for example, thin and round,
but fortunately this data set provides ratings
for playful and calm so we directly incorporate this data.
Then for the positive attribute,
we cluster the attributes in the data set
and obtain two clusters for positive attributes.
We compute weighted average scores for each cluster
and then combine the results for the two clusters
to come up with the scores for the attribute positive.
For serious, exciting and negative,
we use opposite values of the corresponding antonyms.
That is, we simply take one
minus the value of the antonym attribute
because their scores are between zero and one.
The fonts in these images represent
the top five suggested fonts for each affect.
Finally, for trustworthy and disturbing,
we use the averages for the underlying affects
as suggested by the pleasure arousal and dominance
emotional state model.
To evaluate the results, we conducted a within use uh...
within subject user study on Amazon Mechanical Turk,
where we hired 40 participants, all from the U.S..
Each participant completed 50 tasks in total,
consisting of six tasks for each of the eight affects
and two additional validation tasks.
Each task consisted of two congruent, two incongruent
and one neutral option following previous work.
An example task given on the right.
The instructions shown to the participants are:
"Select the image that you think best reflects
the meaning of the word shown in the images."
We see options for two tasks here: serious and disturbing.
The results of the user study are found
to be statistically significant
for each of the eight affects.
The green attributes here show the ones that are found
using antonym relationships
and they seem to have similar performance
to the original attributes directly rated by humans.
For example, the support for exciting
is similar to the support for calm.
The clustering method that is used for positive
is also successful, however,
there seems to be a general trend
towards stronger support for complex emotions.
So the result for positive is not the strongest
among the attributes.
As we see here, the emotion-theoretic approach
that is used for trustworthy and disturbing
received strong support from the participants,
especially for trustworthy.
The support that trustworthy received
is actually much higher than for the two underlying affects
used to compute it.
The study we just saw presented single words to a user.
Next we want to see how this worked out in word clouds.
We conducted a user study to understand the impact
of font on the users' preferences for word clouds.
Overall, this study is similar to the previous one:
we hired 40 participants to do 50 tasks each.
An example for the attribute positive is shown here.
The third and fifth options use congruent fonts
while the second and fourth options use incongruent fonts.
The first one uses a neutral font.
The results for all affects
are found to be statistically significant,
and the results are fairly consistent
with those from the first study.
Both studies show strong support for congruency
with serious and trustworthy.
We again notice that the congruent font options
are more frequently preferred
for complex affects compared to core affects.
Interestingly, disturbing received higher scores
in this experiment compared to the first study.
This might be because of the layout of the word cloud,
which mixes horizontal and vertical orientations,
different font sizes and different alignments.
All these aspects may contribute to making the word cloud
look even more disturbing.
These sorts of effects may also explain
the comparably low support for the attribute calm
in the comparison with the previous user study.
Finally, our third study aimed to understand the impact
of color pallettes on the user preferences for word clouds.
The details of the user study are very similar
to the previous one, where we again hired 40 participants
to do 50 tasks each.
The color pallettes were obtained from Bartram et al.,
and as seen on the right,
we obtained color pallettes using a figure from their study.
A sample task is given on the left,
again for the attribute positive.
Here, the first and third options use congruent color,
that is, positive ones.
The second and fifth options use incongruent colors,
and the fourth one uses neutral colors.
Similar resul-- uh similar to the results of the, uh,
of the previous two studies,
the results for each of the eight affective attributes
are found to be highly statistically significant.
This is particularly strong for calm, negative and playful,
while the results are somewhat less strong
for exciting, serious, positive and trustworthy.
These findings mostly mirror those from Bartram et al..
One exception is exciting,
which did not result in a similarly high preference level
in our study compared to theirs.
Based on all of these results,
we built a prototype of an affect-aware word cloud tool
that recommends congruent fonts and color pallettes.
In addition to the regular text input,
we asked users to specify the affect they want to create.
Fonts and color pallettes that best match
the user's selection are recommended.
When one-- when only a single affect is chosen,
we pick the fonts that have higher scores
for the selected affect and which, at the same time,
do not have high scores for unselected affects.
More details can be found in the paper.
The first column, in the top figure shows top fonts
for positive, when only the selected affect is positive,
which means neither calm nor exciting is selected.
The second column shows top fonts
that are both positive and calm, and so on.
Here we see sample outputs of our prototype
for movies of two different genres.
The cloud on the left is for the movie 'The Smurfs'
while the affec-- with the affect playful.
The cloud on the right is for the movie 'Scream'
using exciting and negative as the affects.
We can see how these signals help create
the intended affects in these clouds.
This example is for the United Nations Wikipedia page
for the attribute trustworthy.
And here are some examples
for positive and negative Yelp reviews.
Our overall conclusion are as follows:
we have presented an approach to
automatically associate fonts with different affects;
we conducted several user studies
and found that across all experiments and attributes
congruent font and color pallette choices
were preferred by a majority.
Based on our experiments,
color pallettes proved particularly powerful
for calm, negative and playful.
In contrast, fonts are especially powerful
for serious, trustworthy and disturbing.
Finally, we showed a prototype
of an affect-aware word cloud tool.
We believe that this kind of font and color exploration
may increase the feeling of creativity in users,
which could be further explored in user studies.
Another aspect that we have left for future work
is to explore and incorporate regional
and culturally-specific font and color pallette preferences.
Thank you very much for your attention.
If you have any questions about the paper,
I recommend emailing the authors,
whose emails are shown below.
Of course, I can only answer questions
in an extremely limited capacity
as I'm only affiliated by university.
Thank you.
(applause)
- [Man] Sam, thanks a lot
for doing the presentation, um...
any questions?
Yes?
- [Woman With Western Europe accent] So I was uh--
hi, thank you for the wonderful presentation.
Uh, my question is do you observe any differences
across users in how they're associating colors
with say emotions?
And do, uh, I mean, so, like, demographics or age, gender
do these factor-- do these actually factor in
when they're deciding what sort of colors
are associated with the emotions?
- So this is a question that I am incapable of answering,
unfortunately because I have almost zero familiarity
with the work, but thank you for the question.
I'm sorry but I-- (laughs awkwardly)
I'm really sorry I can't answer these for them.
- [Katrina] Hey it's Katrina Freizund
from University of Ingolstadt,
thank you for your presentation, um,
I have a question that also experts,
so you are designers and relate the results,
so um was it uh
was it correct with their opinion
about the fonts and the colors?
- To my understanding, I cannot also answer this question.
(audience laughs)
Once again, I am sorry.
- [Man] Let's try to have an easy question, okay?
(audience laughs)
- Excellent, oh
- [Man] There's one
- [Man With Southern Europe accent] Thanks
for your presentation.
So, do you have any information on who chose the colors,
the options that were given to the users?
- The color pallettes that were chosen
were from a previous paper
and they were chosen directly from that paper.
- [Man With Southern Europe Accent] And do you know
if the users could like um select other
um color pallette or something like that?
- I do not know.
- [Man With Southern Europe Accent] Okay (laughs awkwardly)
Thank you.