TagPies: Comparative Visualization of Textual Data

Stefan J

anicke

, Judith Blumenstein

, Michaela R

ucker

, Dirk Zeckzer

and Gerik Scheuermann

Image and Signal Processing Group, Leipzig University, Leipzig, Germany

Faculty of History, Arts and Oriental Studies, Leipzig University, Leipzig, Germany

Keywords:

Tag Clouds, Pie Charts, TagPies, Text Visualization, Text Comparison, Digital Humanities.

Abstract:

A TagPie is a novel tag cloud layout that arranges the tags belonging to multiple data categories in a pie chart

manner. Motivated from research in classical philology, TagPies were designed to support the comparative

analysis of classical terminology. In this scenario, the data categories represent the co-occurrences of different

searched keywords, so that the comparison of the contexts in which these keywords were used becomes pos-

sible using TagPies. This paper illustrates the iterative development of TagPies, which aid as a distant reading

view on a text corpus for humanities scholars. We outline various steps of our collaborative digital humanities

project, and we emphasize the utility of the proposed design by outlining various usage scenarios representing

current research questions in classical philology.

1 MOTIVATION

Traditionally, humanities scholars read texts on pa-

per in order to generate and verify hypotheses about

precisely formulated research questions. As a re-

sult of mass digitization, nowadays, the scholars have

access to large digital libraries containing numerous

texts. This on demand availability of texts changes

the traditional workﬂows of the scholars in different

ways. First, the retrieval of text passages gets eas-

ier, usually, by querying a text corpus using a typi-

cal keyword-based search. The drawback of this ap-

proach is that the quality of results is usually not sat-

isfying. Often, the humanities scholars receive too

many results, which they cannot process individually.

Consequently, it is impossible to generate useful hy-

potheses. On the other hand, the precision can be

low so that many found text passages are irrelevant to

the given research question. Especially in that case,

picking text passages related to the observed topic

is a laborious task. Second, the access to vast tex-

tual data brought forth new research methodologies in

the humanities, introduced by Franco Moretti as dis-

tant reading (Moretti, 2005). Before the digital age it

was inconceivable to generate hypotheses about texts

without explicitly reading them; Moretti presented re-

search questions that were impossible to investigate

with the traditional close reading technique.

In our digital humanities projecteXChange,

the

http://exchange-projekt.de/

collaborating humanities scholars—six historians and

classical philologists—wanted to explore medical

concepts in classical texts, which required workﬂows

that include distant as well as close readings. Work-

ing with the project’s large text corpus, the humanities

scholars are interested in the co-occurrences of med-

ical terms. For instance, they look for terms describ-

ing medical conditions, associated terms for symp-

toms, body parts, etc., in order to explore what ancient

writers knew about the medical concept. The mis-

sion of the corresponding digital humanities project

was investigating novel research questions in classi-

cal philology—the comparison of medical concepts.

For instance, a humanities scholar hypothesized that

the terms morbus comitialis and morbus sacer like-

wise were used to denote epilepsy (a discussion of this

example can be found in Section 5.2).

In order to support the comparative analysis of

medical concepts in classical texts, we developed Tag-

Pies in close collaboration to the humanities scholars

of our project. This paper outlines the steps of the iter-

ative development and the ﬁnal TagPies layout algo-

rithm that includes a tailored tag sorting mechanism

and design features applied to visually separate shared

and individual contexts of terms. To meet the needs

of the humanities scholars, we embedded TagPies as

a distant reading visualization into a visual interface

that is linked to a close reading view, which enable the

inspection of individual text passages in order to as-

sess their relevancy to the observed medical concept.

Jänicke, S., Blumenstein, J., Rücker, M., Zeckzer, D. and Scheuermann, G.

TagPies: Comparative Visualization of Textual Data.

DOI: 10.5220/0006548000400051

In Proceedings of the 13th Inter national Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages 40-51

ISBN: 978-989-758-289-9

We emphasize the utility of this visual interface for

the humanities scholars by providing various usage

scenarios. In a storytelling style, each scenario ex-

empliﬁes how TagPies support verifying and generat-

ing hypotheses concerning philological matters. Ad-

ditionally, we report collaboration experiences gained

during our digital humanities project. This includes

the iterative evaluation of TagPies with the humani-

ties scholars, and limitations of our approach.

2 RELATED WORK

Widely used and perceived as being fun, tag clouds

are important components in the social web to visu-

alize summaries of textual data. Many works present

layout methods developed to consolidate the use and

validity of tag clouds for speciﬁc purposes. Below,

we outline general information about tag clouds, their

use in digital humanities applications, and we take a

look at various tag cloud layout approaches that sup-

port the visualization of multiple data categories.

2.1 Tag Cloud Visualizations

The primary purpose of tag clouds is to present a vi-

sual summary of textual data (Sinclair and Cardew-

Hall, 2008). First introduced by Stanley Milgram’s

mental map of Paris (Milgram and Jodelet, 1976)

in 1976, tag clouds later became popular in the so-

cial web community. Although originally used for

non-speciﬁc information discovery, tag clouds can

also be used to support analytical tasks such as the

examination of text collections (Viegas and Watten-

berg, 2008). Furthermore, tag clouds obtained wide

acceptance as interfaces for navigation purposes on

databases (Hearst and Rosner, 2008). Traditionally, a

tag cloud is a simple list of words placed on multi-

ple lines, either ordered alphabetically or by the im-

portance of a tag, which is encoded by variable font

size (Murugesan, 2007). Portals such as ManyEyes

can be used to create such kind of tag cloud visualiza-

tions on demand (Viegas et al., 2007). A user study

on the utility of tag clouds revealed that the usual al-

phabetic order is not obvious for the observer, but tag

clouds are generally seen as a popular social com-

ponent (Hearst and Rosner, 2008). Potentially, this

was one of the reasons that later more sophisticated

tag cloud layout approaches were developed, which

rather emphasized aesthetics than meaningful order-

ings. A representative technique is Wordle (Viegas

et al., 2009), a popular web-based tool for visualiz-

ing tag clouds used for a wide range of applications.

Wordle produces compact aesthetic layouts with tags

in different colors and orientations, but both features

do not transfer any additional information. The tag

cloud design presented in this paper is based on the

Wordle algorithm. It places the tags similarly using

an Archimedean spiral, but, additionally, we use the

features color and position to visually express the be-

longing of individual tags to and their signiﬁcance for

various data categories.

2.2 Tag Clouds in Digital Humanities

Visualizations in general are widely applied in dig-

ital humanities projects to explore cultural heritage

datasets (J

anicke et al., 2015). Tag clouds in par-

ticular are frequently used to encode the number of

word occurrences within a selected section of a text,

a whole document or an entire text corpus (Vuille-

mot et al., 2009; Fankhauser et al., 2014). For ex-

ample, the VarifocalReader (Koch et al., 2014) uses

tag clouds “to give a visually appealing overview of

a section of text,” which points out the importance

of aesthetics when designing visualizations for dig-

ital humanities scholars. By applying signiﬁcance

measures, the visualization can be limited to display-

ing only characteristic tags, e.g., the most signiﬁ-

cant tags of a selected time period (Eisenstein et al.,

2014) or the most frequently mentioned commodoties

in a text corpus after ﬁltering (Hinrichs et al., 2015).

Topic modeling approaches gain more and more ac-

ceptance in digital humanities applications. Here, tag

clouds help to illustrate the most descriptive tags of

topics (Binder and Jennings, 2014; Montague et al.,

2015). When analyzing the evolution of topics over

time (Cui et al., 2011; Cui et al., 2014), tag clouds

serve to explore the temporal change of a topic’s ter-

minology. In contrast, some tag cloud approaches il-

lustrate trends in a text corpus. Parallel Tag Clouds

generate alphabetically ordered tag lists as columns

for a number of time slices and highlight the temporal

evolution of a tag placed in various columns on mouse

interaction (Collins et al., 2009). SparkClouds attach

a graph showing the tag’s evolution over time (Lee

et al., 2010). Hinrichs links tag clouds to a classiﬁ-

cation schema in the form of a tree structure to help

humanities scholars getting access to texts of a specu-

lative ﬁction anthology corpus (Hinrichs et al., 2016).

The tag cloud approach presented in this paper was

developed in order to support humanities scholar in

comparing the co-occurrences of different classical

terms to each other. That tag clouds are appropriate

visualization to illustrate a word’s co-occurrences is

already shown by Beaven (Beavan, 2008). A basic

visualization that contrasts the co-occurrences of two

words is outlined by Beaven (Beavan, 2011).

TagPies: Comparative Visualization of Textual Data

2.3 Visualizing Categories in Tag

Clouds

Gleicher gives an overview of comparative visual-

ization techniques for different scenarios (Gleicher

et al., 2011). A radial comparative overview of top-

ics whose words are represented by dots is illus-

trated by Havre (Havre et al., 2001). For the com-

parative visualization of tags, various approaches en-

deavor to place related tags close to each other in vi-

sual groups, in the following called data categories.

Thematically clustered tag clouds or semantic tag

clouds support the detection of tags belonging to a

certain topic (Lohmann et al., 2009). As shown by

Schrammel et al. (Schrammel et al., 2009), these tag

cloud designs were often preferred by users for spe-

ciﬁc search tasks as they raise the attention towards

small tags compared to other designs. For traditional

tag lists, semantically related tags of a data category

can be placed subsequently (Schrammel and Tsche-

ligi, 2014), for more sophisticated layouts the usage

of force directed approaches is quite popular. Here,

semantically close terms attract each other (Cui et al.,

2010; Wu et al., 2011; Liu et al., 2014). GMap is a

force directed approach that delivers a segmentation

of a graph into color-coded neighborhoods (Gansner

et al., 2010). Other methods try to preserve seman-

tic relationships in tag clouds by placing the related

tags of each data category in non-overlapping areas

individually. Afterwards, multiple tag clouds are vi-

sually combined to a single one. The Star Forest

method (Barth et al., 2014) initially calculates the lay-

out for the tags of each data category independently.

Then, it uses a force directed method to pack the vari-

ous clouds to gain a uniﬁed tag cloud. In ProjClouds,

a tag cloud layout for each cluster of a document

collection is computed within its assigned polygo-

nal space in the plane (Paulovich et al., 2012). All

above mentioned methods pack multiple tag clouds

together, thus, they can be seen as sophisticated small

multiples approaches since the tag clouds for all data

categories are computed independent of each other.

As a consequence, large in-between whitespaces oc-

cur when composing these clouds to a visual entity.

Words Storms is a rather traditional small multiples

approach computing a tag cloud for each document

of a corpus to support the visual comparison of doc-

uments (Castell

a and Sutton, 2014). Here, a signif-

icant tag for multiple documents appearing in mul-

tiple clouds is placed at similar locations with same

color and orientation. RadCloud visualizes tags be-

longing to various data categories in a shared ellip-

tical area (Burch et al., 2014), but it also suffers

from whitespaces. In Compare Clouds, tags of me-

dia frames are comparatively visualized in a single

tag cloud (Diakopoulos et al., 2015), but the design is

limited to visualizing two data categories. TagSpheres

arrange tags hierarchically on several circular discs to

transmit the notion of distance in tag clouds using a

different color for every hierarchy level (J

anicke and

Scheuermann, 2016). Furthermore, the TagSpheres

layout can be used to visualize tree structures (J

anicke

and Scheuermann, 2017).

3 DIGITAL HUMANITIES

BACKGROUND

This research bears on research in classical philology,

a ﬁeld of the humanities that is concerned with an-

alyzing Latin and ancient Greek texts written in the

classical period. In the following, we outline project

goals and collaboration aspects at project start that led

to designing the proposed TagPies layout.

Project Idea. The purpose of the digital humanities

project eXChange was the development of new work-

ﬂows in order to analyze and to compare medical con-

cepts in classic texts. Due to the digitization era, hu-

manities scholars are now able to browse digital li-

braries using a simple keyword search as a standard

technique to discover related text passages. The cor-

pus of our project combines a multitude of existing

sources such as the Perseus Digital Library

and the

Bibliotheca Teubneriana Latina.

Working with that

corpus, the humanities scholars faced the problem of

retrieving too many results, e.g., a search for morbus

(disease) returned 1,558 text passages. Reading all

text passages and combining the gained insights, es-

pecially, comparing different result sets was not pos-

sible. In close collaboration to humanities scholars

using the project’s text corpus, our mission in the

project was designing an interface, consistent of a

distant reading visualization—TagPies—and a close

reading view, that supports the dynamic exploration

of the results of various keyword-based search queries

in order to facilitate the comparison of various medi-

cal concepts.

Project Start. To ensure designing a valuable, power-

ful tool that supports investigating the posed research

questions, we adopted several suggestions made by

Munzner (Munzner, 2009) for the implementation of

our project. Also, we considered collaboration expe-

riences (J

anicke et al., 2016b) reported by visualiza-

Perseus Digital Library, Ed. Gregory R. Crane. Tufts

University. http://www.perseus.tufts.edu

Bibliotheca Teubneriana Latina. Walter de Gruyter.

http://www.degruyter.com/db/btl

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

tion scholars involved in digital humanities projects

to avoid typical pitfalls when working together with

humanities scholars. We furthermore worked through

related works in the digital humanities providing valu-

able suggestions and guidelines for designing inter-

faces for humanities scholars (Gibbs and Owens,

2012; J

anicke, 2016). To avoid making assumptions

for the design of a visual interface that is hard to com-

prehend and does not support the concerned philo-

logical research questions, we initially discussed the

needs of the humanities scholars and their faced chal-

lenges in the targeted domain in several meetings.

The humanities scholars explained their usual work-

ﬂows, for example, how they use online digital li-

braries for research purposes. On the other hand,

we presented and discussed related text visualization

techniques in order to convey an impression of the

capabilities and challenges within our research ﬁeld.

This get together turned out to be important to un-

derstand each others mindsets, and to deﬁne a set of

workﬂows to compare medical concepts that the visu-

alization shall support.

Requirement Analysis. Having a large text corpus

and the project idea at hand, we began without a

clear visualization idea. We needed several interdis-

ciplinary meetings at the beginning of the project to

specify the research goals of the humanities scholars

and their requirements concerning the visual interface

to be implemented. Initially, the humanities schol-

ars wanted to comparatively analyze classic medical

terminology. They explained, how they would ap-

proach this research task using common workﬂows:

by reading the text passages that contain speciﬁc key-

words. Thus, for a comparative analysis of classic

medical terminology the contexts in which the key-

word terms occur need to be compared to each other.

In a ﬁrst workshop, we presented an overview of text

visualization techniques, and we discussed their po-

tential to support the given research task. It turned out

that some scholars were familiar with the idea of tag

clouds, and basic bar charts were also seen as an ap-

propriate method for comparing word frequencies. As

some of the humanities scholars never worked with

visualizations before, and most of them were not used

to work with complex tools, it was necessary to de-

velop a system, which is easy to understand. De-

spite known theoretical problems (Viegas and Wat-

tenberg, 2008), designing a tag cloud visualization

was the means of choice as they are intuitive, widely

used metaphors to display summaries of textual data.

Moreover, tag clouds have been successfully applied

in digital humanities applications before to analyze

the context of words (Beavan, 2008; Beavan, 2011).

In the following meeting, we discussed a list of re-

quirements of a tag cloud layout to be valuable for

the collaborating humanities scholars. The tag cloud

should (1) support the analysis of the context of a sin-

gle keyword and the comparison of the contexts of

various keywords, (2) communicate the relevancy of

a tag to the keyword it co-occurs with and its rele-

vancy concerning all keywords, and (3) to reﬂect the

proportion of tags from different categories. In a sec-

ond workshop, the humanities scholars worked with

several existing tag cloud visualizations we adapted

to the project corpus. For each queried keyword, we

summarized the frequencies of the co-occurrences.

First, we provided small multiples of Wordle tag

clouds (Viegas et al., 2009), which were seen as aes-

thetic and a good solution to analyze the context of

a single keyword, but a comparative analysis was not

easy as tags co-occurring with various keywords were

hard to ﬁnd. Working with RadCloud (Burch et al.,

2014) yielded exact opposite results: the discovery

of tags co-occurring with various keywords was easy,

but the analysis of a single keyword’s context was

crucial. Here, the approach of visualizing word fre-

quencies in two concurring manners (bar, tag’s font

size) was confusing for the humanities scholars. Par-

allel Tag Clouds (Collins et al., 2009) were unexpect-

edly not favored, although their basic design is simi-

lar to word lists, with which humanities scholars are

used to work. The major issues were the heights of

the tag clouds, which forced the humanities scholars

to vertically scroll many times during the exploration

process. Also, the required interaction to gain addi-

tional information was seen problematic. The human-

ities scholars stated they want to see several informa-

tion “at the ﬁrst glance.” When developing TagPies,

we took the feedback during the second workshop

as well as the importance of aesthetics—often men-

tioned by the humanities scholars—into account. As

postulated by Oelke and Gurevych, we designed Tag-

Pies based upon the above listed requirements derived

from the needs of the targeted user group (Oelke and

Gurevych, 2014).

4 TAGPIES LAYOUT

Given are n data categories d

, . . . , d

(n search re-

sult sets), each containing the co-occurrences for the

queried search terms T

, . . . , T

in the form of tags.

The general TagPies idea is to place the tags belong-

ing to a data category in a speciﬁc circular sector,

forcing vocabulary shared by several data categories

to be placed in the center, and tags unique to a sin-

gle category to be placed in the outer regions of the

tag cloud. With the resultant tag cloud subdivision,

TagPies: Comparative Visualization of Textual Data

the ﬁnal TagPie layout is visually comparable to a

pie chart, which helps the observer to compare the

tag sets of various data categories and to assess their

relative proportions. According to the actual propor-

tions (the number of occurrences of the main terms in

the database) and a maximum number of tags to be

displayed (for the examples in this paper we chose a

maximum of 500 tags), we select the top co-occurring

terms (tags) for each data category. If the relative pro-

portion of a data category is too small, we leave a

minimum of ﬁve tags to be displayed.

For each data category d

, we need to position

the category’s main tag T

(the search term) and the

tags t

, . . . ,t

= {t

|1 ≤ k ≤ |d

|}), which are co-

occurrences of T

. F(T

) encodes the number of oc-

currences of T

in the database, F(t

) denotes how

often t

co-occurs with T

. The relevancy R(t

) of a

tag t

for data category i is deﬁned by

R(t

) =

F(t

)

F(T

)

In the following, we distinguish between shared

tags and unique tags. A shared tag t

has multiple

instances in the TagPie, which are placed in different

sectors. These instances are deﬁned as

I(t

) = {t

} ∪ {t

|1 ≤ j ≤ n, i 6= j,t

= t

and |I(t

)| denotes the number of instances. A unique

tag t

occurs only once in the TagPie as a tag of the

i-th data category, so that I(t

) = {t

} and |I(t

)| = 1.

4.1 Layout Algorithm

In preparation, we order the data categories d

, . . . , d

according to their similarity aiming to place as many

similar tags as possible close to each other. The simi-

larity s(d

, d

) is deﬁned by the number of shared tags

in proportion to the number of unique tags between

two data categories d

and d

as the Jaccard index

s(d

, d

) =

∩ d

∪ d

Initially, we put the two most similar data categories

next to each other in a double-ended queue. Then,

we iteratively determine the data category d

with the

highest similarity to either the ﬁrst (then, we insert d

at the start of the queue) or the last element in the

queue (then, we insert d

at the end of the queue).

With the resultant ordering at hand, we estimate the

amount of space required to place all tags of a data

category. This is achieved by mapping the tags in

the corresponding font sizes dependent on their fre-

quencies, and by adding up the bounding boxes for

Figure 1: Deﬁning circular tag cloud sectors.

all tags. So, we obtain an approximate space require-

ment for each data category. Based on that proportion,

we deﬁne the angles ϕ

, . . . , ϕ

of circular sectors for

, . . . , d

that subdivide a Cartesian coordinate sys-

tem at it’s center (0, 0) as shown in Figure 1.

Main Tag Placement. At ﬁrst, we position the main

tags T

, . . . , T

in the centers of their corresponding

TagPie sectors. To deﬁne these centers, we need to es-

timate the radius r of the TagPie before actually com-

puting it’s layout. Therefore, we compute a Wordle

tag cloud without sectors containing all tags. Using

the obtained radius of this tag cloud as expected ra-

dius r for the TagPie, we can place the main tag T

of a data category d

in the TagPie’s corresponding

sector at position p(T

) = (x

, y

) as illustrated in Fig-

ure 1. Starting with the orientation s, p(T

) is deﬁned

= γ · r · cos(π +

i−1

∑

k=0

)

and

= γ · r · sin(π +

i−1

∑

k=0

With γ = 0.5, we position T

at the center of the sec-

tor. Especially when several small sectors are adja-

cent, the corresponding main tags can occlude. To

avoid these occlusions, we automatically decrease or

increase γ in such cases.

Tag Sorting. The idea of the sorting method is to

place tags with a high relevancy to all data categories

in the center of the TagPie. The farther away from the

center a tag is placed, the more relevant it is to the

corresponding data category. Thus, unique tags shall

be placed in the outer regions of TagPie sectors. To

obtain this ordering, one of the following conditions

need to be fulﬁlled for arbitrary adjacent tags X and Y

in a correctly sorted tag list {. . . , X,Y, . . . }:

C1: |I(X)| > |I(Y )|,

C2: |I(X)| = |I(Y )| and U(X) < U(Y ), or

C3: |I(X )| = |I(Y )|, U(X) = U (Y ) and F(X ) ≥

F(Y ).

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

With C1, shared tags belonging to all data categories

move to the beginning of the tag list, and unique tags

move to the end. In case of even numbers of instances,

tags with low uniqueness values are treated before

tags with high uniqueness values (C2). The unique-

ness of a tag X is deﬁned by the quotient of the two

most frequent occurrence totals of X among all data

categories as

U(X) =

max

∈I(X)

R(X

)

max

∈I(X)\X

R(X

)

The more characteristic a tag X is for a certain data

category compared to the other data categories shar-

ing X, the higher gets U (X). In case of even numbers

of instances and even uniqueness values, C3 ensures

that more frequent tags are processed earlier than less

frequent ones. A ﬁnal step slightly reorders the tags

according to the proportions of the data categories in

the TagPie. As (unique) tags of small data categories

are usually less frequent than (unique) tags belonging

to larger data categories, they are placed at the end of

the tag list after sorting according to the above men-

tioned conditions. In order to ensure that all TagPie

sectors are uniformly ﬁlled with tags, this slight re-

ordering guarantees that tags belonging to small data

categories are treated earlier during the layout algo-

rithm.

With the ﬁnal tag ordering, we iteratively position

all tags following an Archimedean spiral originating

from the tag cloud center at position (0, 0). A tag is

placed if the determined position on the spiral lies in

the sector that is assigned to the corresponding data

category, and if the tag does not occlude other tags.

Otherwise, the tag will be placed in following turns

of the spiral farther away from (0, 0).

4.2 Design

To avoid whitespaces, a problem addressed in (Seifert

et al., 2008), the above outlined layout algorithm is

based on the Wordle algorithm, which permits over-

lapping tag bounding boxes if the letters do not oc-

clude. Thus, uniform, aesthetic tag clouds capable

of visualizing much information compactly inside a

small region are obtained.

Tag design. We use several well-established design

features for the TagPie layout. Evaluated as being the

most powerful property in (Bateman et al., 2008), we

use font size to encode the number of occurrences of

each tag. The visualization of main tags, which are

placed in the center of their assigned sectors, super-

sedes an additional legend, and furthermore it serves

the purpose of accentuating the belonging of related

tags to their category. Main tags are salient due to

bold font style and underlinings. Stated in (Wald-

ner et al., 2013), users perceive rotated tags as “un-

structured, unattractive, and hardly readable.” There-

fore, we do not rotate tags to keep the layout eas-

ily readable to provide an interface that is beneﬁcial

for the collaborating humanities scholars. Also sug-

gested in (Waldner et al., 2013), color is the best

choice for distinguishing categories. Hence, we use

qualitative color maps to assign distinctive colors to

, . . . , d

. For this purpose, we use those qualita-

tive color maps provided by ColorBrewer (Harrower

and Brewer, 2003) that contain solely saturated col-

ors. Here, we consider not to assign red and green

hues as well as colors with similar hues to data cate-

gories of adjacent TagPie sectors.

To visually separate two concurring tag groupings

in TagPies, we considered the Gestalt theory (Ware,

2013). On the one hand, the differentiation between

shared and unique tags was necessary for exploration

purposes, on the other hand, all tags that belong to

a certain data category shall be visual unity. To fa-

cilitate perceiving the former grouping, shared tags

receive a black color while unique tags retain the cor-

responding data category’s color, thus, implementing

the Gestalt principle of similarity. In order to achieve

that the now differently colored tags of a data category

form a visual unity, we applied the Gestalt principle

of enclosure by adding a background shape—colored

in a less saturated version of the color assigned to d

—

that encloses all tags of d

Computing Background Shapes. In order to deter-

mine background shapes for the data categories, we

compute a Delaunay triangulation like illustrated in

Figure 2. Iteratively, we insert the centroids of tag

bounding boxes, and ﬁnally, we receive a triangula-

tion of the tag distribution that contains three different

triangle types. Either a triangle connects three tags

belonging to the same data category, tags of two or

tags of three data categories. Triangles of the former

type are not required for computing background shape

Figure 2: Delaunay triangulation to determine back-

grounds.

TagPies: Comparative Visualization of Textual Data

borders as they lie completely inside the correspond-

ing TagPie sector. When two tags of a triangle belong

to the same data category, we interpolate a line seg-

ment between the bounding boxes as shown in Fig-

ure 2a. For triangles containing dummy nodes of the

super triangle, we do not interpolate line segments,

instead, we link the exterior vertices of the bounding

boxes (see Figure 2b). When all three tags of a tri-

angle belong to different data categories, we generate

three line segments each originating from the triangle

centroid as can be seen in Figure 2c. Finally, the bor-

der of a data category d

—drawn as a Bezier spline—

is composed of the line segments of all triangles that

either contain one or two tags of d

5 USAGE SCENARIOS

TagPies were designed during the digital humanities

project eXChange to support the comparative analy-

sis of medical concepts in classical texts. Figure 3

shows a screenshot of the web-based user interface

the humanities scholars work with. TagPies are em-

bedded as a distant reading visualization that con-

trasts the co-occurrences of various keywords. The

scholar can conﬁgure TagPies by choosing the num-

ber of tags to be shown, and by deﬁning the maxi-

mum distance between a searched keyword term and

considered co-occurrences. After retrieving the re-

sults, stopwords are removed according to stopword

lists provided by the humanities scholars. The re-

maining co-occurrences are visualized in the TagPie.

To facilitate navigation and exploration abilities, we

enhanced TagPies by basic means of interaction ac-

cording to the humanities scholars’ wishes. Of par-

ticular interest was highlighting spelling variants of

words, which are provided by the backend of the re-

search platform. With mouse interaction, we enable

the scholar to detect related tags more quickly. Hov-

Figure 3: Screenshot of the TagPies user interface. The

co-occurrence medicus (doctor) of aeger (sick) is selected.

ering a tag highlights the remaining shared tags and

spelling variants. Additionally, all related tags are

listed in a tooltip (shown on mouse click) that illus-

trates the distribution using a bar chart. By clicking

a tag, a close reading view lists previews of text pas-

sages containing the selected co-occurrence and the

corresponding keyword. The humanities scholars de-

sired this connection to the underlying texts in order

to quickly inspect interesting word relationships.

In the following, we emphasize the beneﬁt of Tag-

Pies for investigating novel research questions in the

humanities that demand distant reading arguments.

We illustrate three usage scenarios provided by col-

laborating humanities scholars, for whom TagPies

turned out to be heuristically valuable for philologi-

cal matters.

5.1 Comparing gibbus and gibbosus

Looking for the term “humpy” in Latin dictionar-

ies, the synonyms gibbus and gibbosus are found.

The ﬁrst example illustrates how a humanities scholar

used TagPies to verify this synonymity. To do so,

she constructed two keyword-based search queries in-

cluding all declensions of the two terms:

gibbus:

gibbarum|gibborum

gibbosus:

gibbosus|gibbosum|gibbosam|gibbosas|

gibbosae|gibbosis|gibbosos|gibbosa|

gibbosi|gibboso|gibbose|gibbosarum|

gibbosorum

The resulting TagPie (Figure 4) provides an overview

of the co-occurrences of both terms. It contains

198 text passages for gibbus and 88 text passages

for gibbosus. The group of black colored, shared

tags in the center of the TagPie illustrates a syn-

onymous usage of the terms concerning the (human)

body, e.g., as they both co-occur with body parts like

pede (foot) or manu (hand), or they are used in the

context of diseases like eye diseases (albuginem, lip-

pus) or broken bones (fracto). More physical terms,

e.g., dorso (back), caput (head) and cerebri (brain),

co-occur only with gibbus. In contrast, numerous

terms related to the ﬁeld of Christian morality co-

occur with gibbosus, e.g., cupiditatis (lust), avarum

(stingy), modestia (moderation) and glorietur (boast).

In combination with close reading several text pas-

sages, the humanities scholar hypothesized that gib-

bus was rather used to describe physical features, and

gibbosus was mostly used in moral contexts. The hu-

manities scholar stated that dictionaries like the The-

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

Figure 4: Comparing gibbus and gibbosus.

saurus Linguae Latinae communicate that both terms

were used in the actual physical meaning of “humpy”

as well as in a ﬁgurative sense. But, as opposed to

TagPies, dictionaries do not report about frequencies

and a tendency in which contexts either of the terms

was used. In that scenario, using TagPies was ben-

eﬁcial as it surpasses the reconstructive character of

dictionaries by referring back to the actual usage of

terms in text corpora.

5.2 Comparing morbus comitialis and

morbus sacer

The second example narrates an unexpected insight

for a humanities scholar who wanted to verify her hy-

pothesis that the terms morbus comitialis and morbus

sacer were both similarly used to describe the disease

epilepsy. Two keyword-based search queries included

all possible cases:

morbus comitialis:

morbus&comitialis|morbi&comitialis|

morbo&comitiali|morbum&comitialem

morbus sacer:

morbus&sacer|morbi&sacri|morbo&sacro|

morbum&sacrum

The TagPie for the posed queries (Figure 5) supported

the scholar in examining three research questions:

• What is the semantic relationship between

both terms? In the center of the cloud the shared

tags graeci – appellant – ιεραν – νοσον, form-

ing the phrase “the Greeks – call it – holy – dis-

ease,” can be seen. This relationship indicates that

both terms were actually used as synonyms for

epilepsy in the classical period.

Figure 5: Comparing morbus comitialis and morbus sacer.

• Were the terms used to describe the dis-

ease in it’s medical or metaphorical meaning?

Whereas morbus sacer, literally translated as

“holy disease,” was rather used as an euphemistic

pseudonym for epilepsy, the co-occurrences of

morbus comitialis instead hypothesize a medi-

cal disease, e.g., shown by remedia (medicine),

medici (doctor), and medentes (curing).

• How was the overall knowledge about the dis-

ease at that time? The co-occurrences for both

terms, e.g., caput (head), insania (insanity), animi

(mind), corporis (body), nervorum (nerves), mor-

tis (death), and abit (died), indicate that epilepsy

was seen as a potentially lethal insanity with phys-

ical symptoms.

In order to generate these hypotheses, the scholar

often used the close reading functionality. When ex-

amining the last research question, the scholar dis-

covered that Maurus Servius Honoratus, a popular

grammarian in the 5th century, mistakenly conceived

epilepsy as a feverish disease, shown by co-occurring

terms of morbus sacer like ignis (ﬁre) or carbunculi

(burning ulcer). As, in the ﬁrst century, Pliny the El-

der already ascertained that fever is not a symptom of

epilepsy, the humanities scholar denoted this discov-

ery as not intutive, so that it would have never been

found using traditional methods.

5.3 Comparing τεχνη, υγιεια and νοσος

The third example investigates the meaning of art

in antiquity, a concept hard to describe nowadays.

The idea at that time was that art can be taught as

TagPies: Comparative Visualization of Textual Data

it includes knowledge. Therefore, art is related to

many ﬁelds in ancient Greek texts (Allen, 1999). Ex-

pectedly, the number of text passages for the ancient

Greek term for art, τεχνη, is enormous (6,216). The

ﬁelds of art are visible in the corresponding TagPie

shown in Figure 6 as co-occurrences: φυσικη (natu-

ral science), μαντικη (art of prophecy), γραμματικη

(grammar), ανθρωπινη (human art), διαλεκτικη (di-

alectic), ρητορικη (rhetoric), ποιητικη (poetics), ια-

τρικη (medicine), μαγικη (magic) etc.

The analysis of the general term τεχνη compared

to the more speciﬁc ancient Greek terms for health

(υγιεια) and disease (νοσος) composing the art of

physicians—medicine—was of particular interest for

one of the humanities scholars. So, she added two

further sections to the TagPie representing 1,013 text

passages for υγιεια and 2,092 text passages for νοσος.

In contrast to the diverse terms surrounding τεχνη, the

co-occurrences here are closely related to their main

terms. Both terms co-occur with parts of the body,

e.g., σωμα (body) and ψυχη (breath, life). Further-

more, υγιεια is related to positive terms like καλλος

(beauty), ισχυς (strength) or ηδονη (enjoyment),

whereas νοσος occurs together with rather negative

terms like λοιμικη (plague), ασθενεια (weakness),

γηρας (senility) or θανατος (death). Also, one of the

known reasons of diseases, poverty (πενια), co-occurs

71 times.

This scenario illustrates the capability of TagPies

for non-speciﬁc information discovery in a distant

reading manner; around 9,000 text passages are sum-

marized and dynamically accessible by clicking co-

occurring terms. This way, new hypotheses can be

generated and veriﬁed—impossible using only tradi-

tional close reading means. For example, the human-

ities scholar discovered a frequent usage of πλουτος

(wealth) in connection with υγιεια (52 times). Look-

ing at the references, ﬁve text passages in the biog-

raphy of Zeno of Elea (Vitae philosophorum, written

by Diogenes Laertius) are listed among others. In this

text, various things are denoted as good or bad. Zeno

of Elea categorizes neither health nor wealth as good,

since both terms can be used also in a negative con-

text. This example reveals another aspect of υγιεια in

a philosophical rather than a medical context. The hu-

manities scholar expected a correlation between med-

ical art and wealth as a consequence of the medical

profession after the 5th century BC in the context of

τεχνη, but πλουτος does not co-occur signiﬁcantly.

Figure 6: Comparing τεχνη, υγιεια and νοσος.

6 DISCUSSION

The proposed tag cloud layout TagPies was designed

to support answering a novel type of research question

in classical philology. Some aspects of the collabora-

tive work are outlined below.

Evaluation. When developing TagPies and the user

interface for the eXChange project, we closely collab-

orated with six humanities scholars—three postdoc-

toral reseachers and three PhD students—who itera-

tively evaluated current prototypes. At ﬁrst, we pro-

vided an interface consistent of a tag cloud showing

the co-occurrences of a single keyword search and

a basic close reading view in order to assess if the

methodology can work for the targeted comparative

approach. After positive feedback, we designed a ﬁrst

circular layout according to the approach outlined in

Section 4. In that version, coloring the tags in depen-

dency on their category was one of the few design

criteria. Tough the humanities scholars were keen

working with the proposed visualization, they had

problems separating unique from shared terminology.

In the following, we prepared and discussed various

design variants, e.g., using different font styles or

adding bar glyphs to shared tags, without ﬁnding a

favorite solution. This situation pushed us to inves-

tigate further on the perception of objects as visual

groups. Finally, following the principles of Gestalt

theory yielded the agreed-upon TagPies design pre-

sented in this paper. In addition to design features,

which also included the selection of an appropriate

font family, the sorting method changed gradually,

so that tags with a particular relevance to an individ-

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

ual data category move to the outer regions of a Tag-

Pie, and shared tags with a similar relevance to mul-

tiple categories are positioned in the center. All in

all, the collaborating humanities scholars of the eX-

Change project evaluated TagPies as comprehensible

and aesthetic, especially the pie chart style was per-

ceived as a suitable metaphor. In addition, we re-

ceived positive feedback from scholars using TagPies

to comparatively visualize textual data for investigat-

ing research questions in other domains. In that con-

text, one scholar rated TagPies as a visualization with

a “broad relevance to the entire ﬁeld of digital human-

ities.”

Limitations. Our main objective was generating aes-

thetic, uniformly looking tag clouds that support in-

vestigating the given research tasks. To gain unifor-

mity, we start the Archimedean spiral to determine all

tag positions at the tag cloud origin. Especially, if a

TagPie consists of many data categories, an adjacent

placement of shared tags cannot be guaranteed. Then,

highlighting shared tags placed far apart requires us-

ing the provided interaction functionality. We ex-

perimented with moving the spiral origin to already

placed instances of shared tags or to borders between

TagPie sectors that share tags, but these approaches

destroyed the intended unity. Sometimes, humanities

scholars are interested in rare cases. TagPies aim to

visualize the most signiﬁcant co-occurrences of the

given search terms. The more occurrences of a search

term exist, the more co-occurrences need to be dis-

played. Then, rare but potentially interesting cases

may be not shown due to the limited number of tags

positioned in a TagPie. Humanities scholars usually

compare a limited number of data categories (up to

ﬁve). In order to assess the scalability of our ap-

proach, we tried examples with more data categories.

Then, pie sectors become very small, so that tags are

hard to position. As a consequence, a sector might

decompose into several components, which destroys

the intended uniformity. Furthermore, effective, qual-

itative color maps are hard to deﬁne for a large num-

ber of data categories. Even though the gained re-

sults were satisfactory, TagPies produce best layouts

for few data categories.

7 CONCLUSION

For the humanities, the digital age brought changes

to the scholars’ research workﬂows. The ability to

query digital libraries in order to receive text passages

containing speciﬁc keywords on demand quickens hy-

potheses generation, but often, vast numbers of re-

sults are hard to process as text passages need to be

checked individually. Tag clouds can be used to facili-

tate the access to search results by aggregating the co-

occurrences of a searched keyword, so that frequent

collacates get salient and the context that deﬁnes the

meaning of a keyword gets visible. TagPies extend

this idea by arranging the co-occurrences of different

keyword searches in a pie chart manner, so that the

contexts in which different keywords occur can be an-

alyzed and compared to each other. This comparative

analysis was the desired capability of TagPies in the

corresponding digital humanities project.

During the development, we closely collaborated

with humanities scholars, who state that the resul-

tant visual interface, consistent of TagPies as a dis-

tant reading view for the results of several keyword

searches and a close reading view for the text pas-

sages, is a valuable analysis instrument that serves a

novel type of research interest that requires distant

reading arguments—the comparison of concepts in

classic texts—and provokes new research questions.

Furthermore, the humanities scholars mentioned that

they have a much more intuitive and dynamic access

to search results when using TagPies in comparison to

working with traditional result lists.

Besides their application to compare the co-

occurrences of words, TagPies are also used to sup-

port investigating other types of research questions

in the eXChange project, e.g., for exploring con-

cept search results of classical terminology (Cheema

et al., 2016a), or to facilitate the close reading of

texts (Cheema et al., 2016b). Furthermore, TagPies

are embedded in the Corpus Explorer

to support cor-

pus linguists in analyzing political texts. In order to

enable a wide applicability, we designed TagPies the

way that it can easily be adapted to textual data of

any domain. Representative examples are outlined by

anicke (J

anicke et al., 2016a), and some can be found

on the TagPies homepage.

ACKNOWLEDGEMENTS

The authors thank all humanities scholars involved in

the TagPies design process. We are furthermore in-

debted to Thomas Efer for maintaining the eXChange

project backend, and for providing interfaces required

to generate TagPies. This research was funded by the

German Federal Ministry of Education and Research.

Corpus Explorer: https://goo.gl/gKWJSX

http://tagpies.vizcovery.org/

TagPies: Comparative Visualization of Textual Data

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