Task-based Evaluation of Sentiment Visualization Techniques
Kostiantyn Kucher
1,2 a
, Samir Bouchama
1,3 b
, Achim Ebert
3 c
and Andreas Kerren
1,2 d
Department of Computer Science and Media Technology, Linnaeus University, V
o, Sweden
Department of Science and Technology, Link
oping University, Norrk
oping, Sweden
Computer Graphics and HCI Group, Technical University of Kaiserslautern, Kaiserslautern, Germany
Sentiment Visualization, Sentiment Analysis, Visual Variable, Visual Representation, Visual Encoding, User
Study, Text Visualization, Visual Analytics, Information Visualization.
Sentiment visualization is concerned with visual representation of sentiments, emotions, opinions, and stances
typically detected in textual data, for example, charts or diagrams representing negative and positive opinions
in online customer reviews or Twitter discussions. Such approaches have been applied for the purposes of
academic research and practical applications in the past years. But the question of usability of these various
techniques still remains generally unsolved, as the existing research typically addresses individual design al-
ternatives for a particular technique implementation only. This work focuses on evaluation of the effectiveness
and efficiency of common visual representations for low-level visualization tasks in the context of sentiment
visualization. More specifically, we describe a task-based within-subject user study for various tasks, carried
out as an online survey and taking the task completion time and error rate into account for most questions.
The study involved 50 participants, and we present and discuss their responses and free-form comments. The
results provide evidence of strengths and weaknesses of particular representations and visual variables with
respect to different tasks, as well as specific user preferences, in the context of sentiment visualization.
Information visualization (InfoVis) is widely used as
part of various data analysis and presentation work-
flows, with a strong interest demonstrated within
and beyond the academic environment (Fekete et al.,
2008). In order to reach the goals intended by
the users or the audience of such visualization ap-
proaches, design choices have to be made with respect
to the task, data, and limitations of human perception
and cognition (Carpendale, 2003; Amar and Stasko,
2005; Munzner, 2015). All of these concerns are
valid and relevant within the area of sentiment visu-
alization, which is concerned with (interactive) visual
representation of sentiments, emotions, opinions, and
stances. While these concepts are not exactly identi-
cal (Munezero et al., 2014), they are related and are
often used interchangeably as part of practical appli-
cations dealing with detection and analysis of subjec-
tivity. The main modality of interest for such analyses
is text data, and the possible data sources and appli-
cations include customer reviews, social media posts,
etc. Various computational approaches have been pro-
posed to detect and categorize sentiment in such data.
The typical task is to classify the polarity/valence of
a given text item (sentence, document, etc.) as neg-
ative, neutral, or positive, although further alterna-
tives exist with regard to the categories (e.g., emotions
such as anger), scope of analysis, etc. (Mohammad,
2016). The visualization techniques complementing
such computational analyses have also been discussed
in the literature (Shamim et al., 2015; Kucher et al.,
2018). However, the previous works on sentiment vi-
sualization have been limited with respect to the evi-
dence on usability (Frøkjær et al., 2000) of such tech-
niques for specific tasks. Discovering the respective
evidence from empirical data would be an InfoVis
evaluation task (Isenberg et al., 2013).
In this work
, we aim to address the research
gap on sentiment visualization evaluation by col-
lecting and analyzing the evidence of the us-
ability of common design alternatives for partic-
ular user tasks. More specifically, we focus on
Based on a thesis project (Bouchama, 2021).
Kucher, K., Bouchama, S., Ebert, A. and Kerren, A.
Task-based Evaluation of Sentiment Visualization Techniques.
DOI: 10.5220/0010916400003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 3: IVAPP, pages
ISBN: 978-989-758-555-5; ISSN: 2184-4321
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
the questions of effectiveness and efficiency of
visual metaphors/representations and visual vari-
ables/channels for visual encoding of sentiment and
emotions. These research questions are relevant to
the basic choices affecting the design of visualization
techniques. To answer them, we design and conduct
a task-based within-subject user study involving vi-
sual representations of sentiment, while taking the er-
ror rate and task completion time into account. The
study was carried out online, and the responses and
free-form feedback were collected from 50 partici-
pants in May–June 2021. The findings of our work
can be applied for choosing sentiment visualization
strategies for communicating the results of computa-
tional and/or manual sentiment analysis methods.
In this section, we discuss the important concepts and
previous works relevant to the problem of evaluating
sentiment visualization.
2.1 Visual Representations
The choice of particular visual representa-
tions/metaphors is dependent on the context of
their usage. As G
org et al. discuss, there are multiple
different visual representations available—from
simple and complex over to univariate and mul-
tivariate representations, with theoretical design
considerations and empirical evidence suggesting the
feasibility (and usability) of particular representations
for particular tasks (G
org et al., 2007). Common
visual representations are, for example, bar charts,
scatter plots, pie charts, line charts, etc.
Visual representations make use of low-level
graphic elements (marks) and visual variables to con-
vey the information to the user. Visual variables rep-
resent attributes of graphical marks that are easily
processed by the human (G
org et al., 2007). Bertin
defines the following seven visual variables: posi-
tion, form, orientation, color, texture, value, and
size (Bertin, 2011). These variables are distin-
guished perceptually without the use of cognitive
steps in contrast to comparing written numbers, for
instance (G
org et al., 2007). However, choosing the
right visual variables depends on the underlying data
that is to be visualized. Munzner discusses two im-
portant principles for using visual channels and rep-
resentations: expressiveness, meaning that the encod-
ing should aim to represent all the information present
in the data, without misleading the user; and effective-
ness, meaning that the importance of the data attribute
should match the saliency/noticeability of the visual
channel (Munzner, 2015). These considerations will
play an important role in this work.
2.2 Sentiment Visualization Techniques
The existing sentiment visualization techniques have
been discussed in the literature in the respective re-
views and surveys (Boumaiza, 2015; Shamim et al.,
2015; Kucher et al., 2018) as well as in the larger
context of text visualization, visual text analytics,
and social media visual analytics (Kucher and Ker-
ren, 2015; Chen et al., 2017; Alharbi and Laramee,
2019). The prior research establishes several catego-
rizations of such sentiment visualization techniques,
which typically include the dimension of the source
data domain (such as customer reviews or social me-
dia posts), the dimension of sentiment categories or
quantitative values (e.g., positive vs negative, a list
of basic emotions, etc.), and the choice of visual rep-
resentations used by the respective technique. Com-
mon visual representations such as bar charts and line
charts can be used for these purposes, but also novel
metaphors (Shamim et al., 2015). Furthermore, some
of the existing surveys discuss the choice of visual
variables for representing the actual sentiment cate-
gories or values. Kucher et al. report that the ma-
jority of techniques in their survey use the visual
variable of color for this purpose, followed by posi-
tion/orientation and size/area (Kucher et al., 2018).
Whether a particular combination of the visual repre-
sentation and visual variable fits the user tasks asso-
ciated with sentiment visualization is then the ques-
tion to answer—and in order to provide the respec-
tive guidelines not only from the position of general-
purpose information visualization principles, but sen-
timent visualization in particular, there is a need to
collect further empirical evidence, as discussed next.
2.3 Evaluation of InfoVis Approaches
Evaluation is typically mentioned in visualization re-
search as an umbrella term for various forms of
validation, ranging from use cases and domain ex-
pert reviews to longitudinal case studies and con-
trolled lab experiments (Carpendale, 2008; Lam et al.,
2012; Isenberg et al., 2013; Elmqvist and Yi, 2015).
In human-computer interaction (Ebert et al., 2012),
“evaluation” (focusing on estimating the usability and
collecting user feedback for mainly formative pur-
poses as part of an iterative design-implementation-
validation process) is sometimes contrasted to “ex-
periment” (typically summative purposes and a con-
trolled environment) (Purchase, 2012).
IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications
Regarding examples of existing evaluations of
sentiment visualization, for instance, Diakopoulos et
al. discuss the feedback of the target audience (jour-
nalists) of their tool Vox Civitas focusing on the
perceived effectiveness and usefulness (Diakopoulos
et al., 2010). Zhao et al. complement a more open-
ended participatory study with a crowdsourced task-
based study for their emotion visualization approach
PEARL, providing empirical data on the usability of
the tool for particular tasks (Zhao et al., 2014).
Prior results covering multiple techniques and rep-
resentations for sentiment visualization are limited,
though. The review by Boumaiza includes multiple
techniques, but does not provide a systematic catego-
rization or comparison (Boumaiza, 2015). The work
by Shamim et al. reveals findings on the usability
of opinion mining systems’ visualizations (Shamim
et al., 2015). They classify 11 techniques accord-
ing to the visual metaphor, including bar charts,
rose plot variations, etc. Their work compares tech-
niques concerning different metrics such as being eye-
pleasing, easy-to-understand, user-friendly, etc. They
conducted a questionnaire survey, and data was col-
lected via this questionnaire and through seminars.
They conclude that simple, easy-to-understand, low-
dimensional visualizations are rated higher than the
others. Shamim et al. rank the top five representations
included in their study as follows, according to the
users’ preferences: bar chart, glowing bar, treemap,
line graph, and pie chart. These results are interest-
ing, but while they are based on the reported pref-
erences, they do not provide evidence about the us-
ability (Frøkjær et al., 2000) of such representations
for user tasks in the context of sentiment visualiza-
tion. In our study, we aim to focus on two aspects
of usability: effectiveness and efficiency. As dis-
cussed by Frøkjær et al., effectiveness is assessed by
determining accuracy and completeness with which
users achieve certain goals; for instance, this could be
achieved by measuring the error rate of user responses
compared to the ground truth data available to the ex-
perimenter (Purchase, 2012). Efficiency is calculated
by the relation between the accuracy and complete-
ness with regard to the resources used to complete the
task, for instance, the task completion time. These
aspects are taken into account as we continue the dis-
cussion of particular user tasks for our study.
In this section, we describe the experimental design
of our study, including the particular user tasks, visual
representations, datasets, and implementation details.
3.1 User Tasks and Alternatives
Our within-subject study was targeted at the general
public to facilitate the generalizability of the findings
and to increase the potential number of participants.
This required all the tasks and visualizations to be in-
tuitive to the average user, whose level of visualiza-
tion literacy might be limited. The design of our study
was inspired by the previous work on task-based eval-
uation of common visual representations (Saket et al.,
2019), and it involved several user tasks related to
sentiment visualization in our case. To visualize senti-
ment and emotion, we used several sentiment analysis
datasets depending on the task. Based on these visual-
izations, we came up with different survey questions
for each plot. The particular choice of the tasks was
based on the work by Amar et al., who proposed a set
of ten low-level analysis tasks that describe users’ ac-
tivities while using visualization tools to understand
their data (Amar and Stasko, 2005). In our study, we
focused on a subset of seven tasks relevant to senti-
ment visualization and common visual encodings:
Find Anomalies: e.g., Does the data look abnor-
Find Clusters: e.g., How many clusters can you
identify in this plot?
Find Correlation: e.g., Is there a (weak/strong)
correlation between sentiment intensity and time?
Compute a Derived Value: e.g., What is the sum
of all negative and neutral sentiments in the pie
Find Extremum: e.g., What sentiment has the
highest/lowest value?
Filter: e.g., How many emotional states can you
identify between time step X and Y?
Retrieve Value: e.g., Which emotion has green
color in this plot?
To address the research questions of our work, we
aimed to propose the study participants to solve these
tasks while using various combinations of visual rep-
resentations and visual variables. In particular, we
focused on the following five standard visual repre-
sentations: scatter plots, histogram charts, bar charts
(besides histograms), line charts, and pie charts. Vi-
sual variables associated with representation of sen-
timent/emotion data were thus considered in relation
to a particular encoding and data. Finally, we should
mention that interaction was beyond our particular re-
search questions for this study, and thus the respective
tasks were designed for static visualizations only.
Task-based Evaluation of Sentiment Visualization Techniques
Figure 1: One of the study tasks with the question text How
many neutral tweets were posted between Apr 26 and Apr
28? and answer options 18, 14, 10, and Don’t know.
Figure 2: Responses for the demographic questions.
3.2 Data and Implementation
Our study used two datasets introduced in the prior
works as the basis for generating the visualizations for
particular representations and tasks. The first dataset
is a subset of Amazon reviews (Nibras, 2019), which
includes prices, reviews, and scores for cell phone
items. The second dataset is a Twitter dataset (Mo-
hammad et al., 2018a; Mohammad et al., 2018b),
which consists of three different subsets. The first
subset consists of tweets with a sentiment/intensity
score between 0 and 1. The second subset consists of
tweets categorized by one of seven different intensity
classes, from very negative emotional state can be in-
ferred to very positive emotional state can be inferred.
The last subset classifies each tweet into eleven fine-
grained subjectivity categories (emotions and further
aspects of subjectivity): anger, anticipation, disgust,
fear, joy, love, optimism, pessimism, sadness, sur-
prise, and trust. Based on these datasets and the
considerations discussed above, 26 unique plots were
generated in total (see an example in Figure 1), and
these were used for 28 questions (several plots were
re-used for different tasks, e.g., extremum vs retrieve).
To facilitate the accessibility of this study targeted
at the general public, a decision was made to imple-
ment it as an online tool, which could be used from
most web browsers on most platforms. The partic-
ular visualizations were implemented in Python and
exported as static figures. For most visualizations,
Plotly for Python, Bokeh, and Matplotlib were used,
and for the data handling, pandas was used. The
online survey itself was implemented with an open-
source JavaScript library survey.js and deployed as a
DigitalOcean web application using React.js.
In order to test the feasibility of both the survey
questions/tasks and the technical implementation, a
pilot study was run with three invited participants
with no visualization knowledge. Based on their feed-
back, the implementation was adjusted and technical
issues were resolved. Afterwards, the online survey
and the respective instruction pages were made avail-
able online for the general public and invitations were
sent out through several university mailing lists. The
results of the study are presented next.
In this section, we focus on the user participation, par-
ticular task results, and the reported feedback.
4.1 Overview
The online questionnaire was run between May 12
and June 6, 2021, and the results include the re-
sponses from 50 participants. They were informed
on the start page that the participation was fully vol-
untary and could be withdrawn at any time, while
their answers and response times would be recorded
for future use. Participants were able to contact one
of the authors for any further information regarding
the study. No personal information except for the de-
mographic question responses was recorded, and no
compensation was offered to the participants.
The initial demographic question responses are
summarized in Figure 2. Next, most participants re-
ported that they had limited (18 participants) or no (14
participants) experience with visualization; 47 partic-
ipants reported no color blindness issues, while the
other 3 participants reported mild issues; 35 partici-
pants took the survey on their computer or tablet, and
the other 15 participants took it on their phones.
The participants were then asked to solve three
warm-up questions and were warned that the answers
and response times would be recorded afterwards.
The responses for the next 28 single-choice questions,
which were designed according to the considerations
discussed in Section 3 and displayed to the partici-
pants in a shuffled order to minimize learning effects,
are discussed in the following subsection.
IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications
Figure 3: Average time per task vs. the average error rate
per task. The task name is encoded using color and visual
representation as the marker shape.
Figure 4: Different task types vs. the average error rate per
task. Color encodes the representation. The horizontal lines
represent the mean of the average error rates per task.
4.2 User Task Results
The majority of participants answered 90% of the
questions right. The average total time spent by par-
ticipant is 10 minutes, and the average response
time is 20 seconds per question. Figure 3 visu-
alizes the distribution of the average error rate and
time per task. The question with the lowest error rate
(0%) is What phone brand has the lowest average rat-
ing? (the extremum task / a bar chart), and it also has
second-lowest response time (12 seconds).
Figure 4 focuses on different task types vs. the
average error rate. There is only one outlier here: a
question from the correlation task (From the above
graph, can you tell that there is a (weak) correlation
between ratings and the number of reviews?) with a
line chart. This may be due to the fact that this ques-
tion was rather hard to be answered by the general
audience. Participants were the most effective in the
following tasks, sorted from most to least effective:
Figure 5: Average response time per task with representa-
tion encoded with color. The horizontal lines represent the
mean of the average response times per task.
retrieve, extremum, filter, derived, cluster, and corre-
lation. The anomaly detection task is left out here be-
cause it was represented by only two questions, which
might not be sufficient to estimate its effectiveness.
Figure 5 demonstrates the average response time
per task. The clustering task has a question with the
highest average response time of 48 seconds. The
fastest response time is observed from the anomaly
task using a line chart (12 seconds). Without the
outlier of the cluster task, pie charts also tend to be
around the average response time of 22 seconds. Sim-
ilar can be observed for histogram plots. Regarding
the task results in relation to the demographic infor-
mation discussed above, our data suggests that the
gender of participants neither had an influence on re-
sponse time nor the correctness rate. The results for
the educational status and device type are mixed, too.
4.3 User Preferences and Feedback
The final part of the study included several questions
about the participants’ confidence and preferences. 38
participants reported moderate or high confidence re-
garding their responses. Next, the participants were
asked to rank the visual representations from easiest
to hardest to work with, in their opinion
. The results
for this question were somewhat heterogeneous, with
4 responses supporting the ranking of (bar charts, his-
tograms, line charts, pie charts), 3 responses for three
different rankings, and 2 or 1 responses for other rank-
ings. The next question was about the color coding
used in the user tasks. 36 participants stated that some
colors were easier to work with than others, 10 stated
that they did not perceive a difference in efficiency,
Scatter plots were not included here as they were only
used in two tasks in the study.
Task-based Evaluation of Sentiment Visualization Techniques
and 3 stated they did not know, while one participant
skipped this question. The next multi-choice ques-
tion was What color (in general) would you find best
for identifying positive and negative emotions? with
several proposed combinations as well as the Don’t
know and Other. Here, 46 participants stated that
their color preference is Green for positive and red
for negative sentiment; 4 participants chose white for
positive and black for negative; 4 participants stated
other preferences, and two of them wrote afterwards:
“I don’t care as long as it’s consistent over all plots”
and “Green for Positive, Black for Negative. Not as
intuitive, but looking better on a screen..
As part of the final free-text remarks, we recorded
the following responses, among others:
“Didn’t understand the usage of question 41 [pre-
ferred ranking of representations, see above] ini-
tially and didn’t find a way to correct it. Correct
order would be 1: Bar Chart, 2: Pie Chart, 3:
Line Chart, 4: Histogram Chart. Also, a small vi-
sualization at this point would help tremendously
to recap which was which. In the question about
what was looking odd in the pie chart, I missed the
option ’only one text was white, the others black’.
“Having to look at and deal with pie charts is a
cruel and unusual punishment.
“In general it was a good survey. When I was
using my phone for the first attempt I misclicked
and wanted to go back, but there is no ’previous’
button. It’s very quick shift between questions, I
wasn’t very sure that it registered the answer I
In this section, we discuss the outcomes and limita-
tions of our study, which might provide guidance on
the design of sentiment visualization approaches and
inspiration for further research on this topic.
5.1 Study Outcomes
By analyzing the results from a different perspective
(see Figure 6), we can note that the users were most
effective with the following representations (in de-
creasing order): bar chart, histogram plot, pie chart,
line chart, and scatter plot. However, we should note
that the survey questions only included two scatter
plots. Thus, the observation of the effectiveness of
that particular representation might not be reliable.
By investigating Figure 5, it is clear which tasks
seem to be answered with shorter response times,
Figure 6: Average error rate per question categorized by
representation. The horizontal lines represent the mean of
the average error rates per representation.
Figure 7: Average response time per question categorized
by representation. The horizontal lines represent the mean
of the average error rates per representation.
thus being more efficient than others in the context of
sentiment visualization. The simpler tasks regarding
the average response time in ascending order are re-
trieve, anomaly (note: used in two questions only),
extremum, derived, correlation, cluster, and filter.
With a similar approach, we get the most efficient
representations from Figure 7 (in descending order):
scatter plot (note: used in two questions only), bar
chart, histogram plot, line chart, and pie chart. To
get to a more fine-grained analysis of efficiency, we
take a look at some specific questions. The question
which took the participants the longest to answer is
from the cluster task, What are the three main clus-
ters of emotional states? using a pie chart, with an
average response time of 48.6 seconds.
For the efficiency of visual variables, we observe
similar patterns as with effectiveness. Users tend to
identify the polarity of sentiments faster when we
used green for positive and red for negative emo-
tion. In terms of neutral polarity, we could not iden-
IVAPP 2022 - 13th International Conference on Information Visualization Theory and Applications
tify differences in efficiency. However, many partic-
ipants noticed graphs where polarity seemed to have
a “wrong” color. This means that they had expected
that some sentiments should have another color than
the one used. As we used many different colors in our
graphs, there is no clear correlation between a particu-
lar color encoding and other less/more efficient visual
channels. We thus come to the following conclusion
with regard to channels, ordered from most to least
efficient: color, bar size, and point position.
As our survey also showed some interesting re-
sults on user satisfaction (Frøkjær et al., 2000), pref-
erences, and free-form user feedback, we also take
some short notes on these results. First of all, most
of the participants stated that they were moderately or
very confident in answering the questionnaire. The
free-form feedback for the technical implementation
and contents of the survey was overall very positive,
and only a few statements were made about some con-
fusing design choices. Regarding the preferences for
particular colors, many users stated that they found it
easier to work with green/red and light blue colors for
positive, negative, and neutral emotions. Only a mi-
nority stated that color did not influence their perfor-
mance and satisfaction. Thus, we suggest consider-
ing this color scheme for sentiments/emotions (while
keeping color blindness concerns in mind).
It is also interesting to notice that the participants
identified the polarity of the sentiments/emotions of
surprise and trust differently. Only 11 participants re-
garded these two sentiments as positive, while 36 par-
ticipants stated that only one of these sentiments have
positive polarity and the other neutral polarity. 28 par-
ticipants stated that trust has positive, and 12 stated
that this sentiment has neutral polarity. It clearly
shows that every participant defines the polarity of
these categories differently.
5.2 Limitations and Open Challenges
There are some limitations of our work which can be
taken into account for further research efforts. First of
all, the implementation of the study as an online sur-
vey rather than an experiment in a controlled lab set-
ting might have implications for the reliability and re-
producibility of the results (Fekete and Freire, 2020).
We aimed to mitigate some potential issues by intro-
ducing several mandatory warm-up questions before
the main time-tracked part of the study. Several mi-
nor technical issues were reported, for example, sev-
eral participants had issues with the ranking question
interface, which might have affected the reported re-
sults on the users’ preferences to some extent.
The participants in this study were asked to com-
plete tasks using static visualizations only. We en-
courage further research on the effectiveness and ef-
ficiency of sentiment visualization techniques while
taking interactivity (Munzner, 2015) into account.
The number of visual marks shown in the visualiza-
tions in this study was also limited to a rather small
number, as it could require increased duration and
complexity of the study. Future research should look
at how the data point cardinality affects the users’ per-
formance in the context of sentiment visualization.
This could involve replication of this study with larger
datasets, larger numbers of unique plots and ques-
tions, but also a larger number of participants. Em-
pirical data concerning further representations, poten-
tially involving additional visual variables/channels
and even 3D representations, would also be valuable.
Finally, we should remember the concern dis-
cussed by Isenberg et al. about the evaluation of com-
plex visualization / visual analysis approaches, which
cannot be reduced to controlled experiments focus-
ing on individual visual representations and low-level
interactions (Isenberg et al., 2013). This challenge
is still open with regard to sentiment visualization
problems—thus, further evaluation of such complex
and feature-rich approaches and solutions that involve
sentiment and emotion data visualization (with the re-
sults and guidelines highlighted in this work in mind)
remains as another opportunity for future research.
This study investigated effectiveness and efficiency
of common visual representations and variables for
various user tasks in the specific context of senti-
ment visualization. We conducted an online task-
based within-subject user study with 50 participants
from the general audience. The study involved static
sentiment visualizations with several common visual
representations and seven user tasks, resulting in 28
questions and further input on users’ preferences and
feedback. After the analysis of the results, we dis-
cussed how different visual variables and representa-
tions influence the mentioned usability measures, and
we also outlined design recommendations and oppor-
tunities for further research on this topic.
This research has been partially supported by the
funding of the Center for Data Intensive Sciences and
Applications (DISA) at Linnaeus University. The au-
thors would also like to thank the study participants.
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