1
H
ow to make
sense
of the Leximancer
analysis
: a q
uick
g
uide
to interpreting concept maps
This
quick
guide has been developed
to
support
the
research
process
es
of those who
are new to
the
semantic analysis software
Leximancer and would like to make a prompt
s
tart with
interpreting
the
data.
The guidance is mainly
focused
on
making sens
e
of
the
concept
maps and
gives
some tips on what to look for when exploring
the data.
Although
a couple
of
‘
how to do
’
bits of
advice
are
also
included,
generally
we assume
that
the
resea
rcher
would
know how to reach
the
‘
output
’
(actual concept map) stage
. This guide
is based on the
latest
Leximancer 4
v
ersion
,
so
the
interface could be slightly different
from the
early
version
s
of
the
software
,
but
the
general
principles of
the
analysis
and core functions of
the
software remain
the
same
.
1.
What is Leximancer?
This is a
semantic
analysis
software
that
h
as been developed at the
University of Queensland, Brisbane
in 2001
(
https://www.leximancer.com/
).
2.
Why
/
w
hen
would
you want to use the software?
Leximancer is a u
seful instrument for
researcher
s
/analyst
s
who need
to
explor
e
a
large
text
-
based data
set
where
manual analysis
and
coding
would be too time consuming
,
e.g. qualitative survey
data
,
multiple interview/focus group transcripts
, lengthy reports or
web
-
based textual information
.
3.
How does it work?
The process is called
unsupervised semantic mapping of natural
language
.
The
method can also be thought
of as a form of text mining
.
Leximancer
employs two stages of
information extraction
:
semantic
and
relational
,
using a different algorithm for each
stage.
It
computes
the frequency with which each
word
is used
and then
calculates
the
distance between each of the terms
(co
-
occurrence
)
.
The algorithms
used are statistical, but they employ non
linear dynamics and machine
learning
.
The results of computations are displayed
as a concept map
that can be explored
on in
divi
dual concept level
s
and
also by looking
at
the
family of associations
between
different
concepts
(themes)
.
4.
What are
the
benefits of using
the
software?
L
eximancer
provides a fairly unbiased method of revie
wing complex
textual data sets
and a clear
process of justifying decisions about text
selection
.
It
make
s
the
researcher
aware of the
‘
global
’
context and
helps
to
d
iscover
‘
hidden
’
structures in text
that fall outside of
his/her
preconceived
framework.
2
T
he a
utomated analysis
demonstrates a
stability of measurement over
time
and what is most important
-
it
allow
s
a
more rapid and frequent
exploration of
text
with r
educed cost
.
Tailored
(researcher driven)
analysis also could be done
if
the
researcher
would like to explore
specific
topics/
concepts.
O
ne of
the
attractive features of
the
software is its ability to identify
sentiments by
showing
probability of a concept being mentioned in
a
favourable
or unfa
vourable
context.
Leximancer
could be used on its
own or
as a complementary tool for other methods of analysis.
5. Anything to
be aware of
/any limitations
?
There are some limitation
s
to
‘
purely automated
’
analysis.
Some
c
oncepts emerge strongly where they are represented by a narrow
vocabulary.
Others
will be
identified from
a broader pool of terms and
ha
ve
a greater likelihood of being diluted as concept
s
in the map.
This
can be mitigated
by the
manipulations
with
a
thesaurus
(e.g. adding
concepts that haven
’
t make a relevance threshold to
the
concept map,
cre
ating combined
/
compound
concepts
etc)
and should be
acknowledged as a researcher
-
driven analysis.
Leximancer findings might benefit from being combined with outcomes
from other type of analysis such as traditional thematic analysis or
content analysis for gaining a more comprehensive picture.
Some more insight
s into the software and how it works
could be found
on
http://www.textinsight.net/sites/default/files/files/What%20is%20Lexim
ancer.ppt
T
he most exciting part is
actually using the tool!
First of
all...
First of all you need to run a preliminary analysis
of
your
‘
unspoiled
’
data set,
with no editing or
configuration
,
to get a
‘
feel
’
of
the
data.
If you are using
the
software for
the
first time, f
or a step by step
guidance
of how to
make a start
look at the
PowerPoint
presentation
created by
Julia Cretchley
and
Mike Neal
http://www.textinsight.net/sites/default/files/files/First%20Leximancer%
20A
nalysis.ppt
6.
How to
make sense of your first
concept map
Concept level
exploration
3
1.
Take
some
time to visually explore the concept map
generated
(you might want to
set
the
Theme Size scrolling Bar
on
0%
(
see Fig
. 1), so that you can
focus
on
the
concept
s
first.
Fig 1.
2.
When you
have
located the most
re
levant
concept (largest circle)
,
you
will
instantly
know what is of the most importance to
either
your
re
spondents/research participants
or to the text creators.
P
ay attention to the
concepts
positio
ned closely to
the
most
relevant
concept
-
t
hose with direct links/connections
would
indicate that these two
words
are
often used
together
and
worth
y
of detailed exploration
.
Things to note:
Wa
tch
out for language your research participants use.
S
tudents
often
use different terms to describe the same concept
-
it
might give you an indication of a specific connotation
or
a context
in
which
they choose to use
a
particular word.
For example
while
describing
academic
staff, students use different wor
ds such as
staff,
lecturers, tutors
,
teachers
etc. W
hat is interesting
is that
these
concepts/themes are not always located close to each other and
associated with a different
contextual and often
emotional
background.
4
3.
Explore other concepts (
those
that
constitute ‘nodes’ in the concept map)
. Their
relevance is reflected by size
(more relevant ones have bigger size)
. Note
where
they are positioned
on
the
map
and which
concepts they are
surrounded
by
. This
could
give you an idea of
other
significant
themes
and
how they are connected
.
4.
Look for
concepts
that are
less
important/could be excluded from
your analysis,
(these
, for example,
could be frequ
ently mentioned words
such as
‘
certain
’
,
‘
have
’
etc). Some concepts
could be
merge
d
(e.g. placement and placements)
or
ma
de
compound
(e.g. work +
experience
)
.
You could later make these
changes to
make
your concept map more readable
5.
E
xplore
the
Concept Ranking Table
located to the right from
the
concept map
(Fig 2)
.
T
he
relevance of the
concepts will be
indicated
in
the
table
numerically
(
number of
instances
/actual quotes
and
relevance
%)
.
Fig 2.
6.
By clicking on a concept on
the
map you can
see
how this concept is related to
others. It will be shown via
graphical
links and
also as a table indicating the likelihood
of
other
concept
s
being
mentioned together wi
th
the concept in question
(Fig 3)
.
5
Fig 3.
7.
When you
familiarised yourself with
the
layout of
the map and
have
a broad
understanding
of the
concepts
that are coming out from
the
data
and how they are
connected
,
it's
time
to explore the direct quotes/instances tha
t
formed
these concepts.
Go to the
Concept Ranking Table
(
located to
the
right from concept
map)
and under
the
Count
heading
c
lick on
a number
that
corresponds to the concept you would like
to explore
(Fig
4
)
. You will have instant access to a
ll the quo
t
es that
contributed to
the creation of
the concept.
The
quotes
will
give you an ind
ication of
the
meaning
(s)
behind the concept and
essentially
how ‘
semantically clean
’
is the
concept.
For
example
word
‘
feedback
’
would
most likely
have
a
singular meaning
(Fig
5
)
, while
‘
work
’ might
have multiple meanings,
since
students
, for example,
could talk about
their course
work, part
-
time work, work related learning, work as verb
–
e.g. ‘work it
out’ etc.
In case of multiple meaning
s
you might want to explore only specific
links/connections with
other
concepts
that ar
e of interest for your research or make
some adjustments
on
the
next, advanced stage of
the
data processing.
6
Fig
4
.
Things to note:
Not all
possible
quotes
where the concept (word) was
mentioned in
the
text
wil
l
be
included in the list. Only those that
passed the relevance threshold will be listed.
Fig 5
.
7
8.
If you are interested in
quotes/instances where two specific c
oncepts were
mentioned together
,
c
lick on a particular concept (under
WordLike
)
i
n the Concept
Ranking table
,
and when a list of rela
ted concepts appeared with
the icon
,
click
on
the
icon
attached to the concept
that
you would like to explore together with the
first concept.
(Fig
6
)
Fig
6
.
Exploration of
themes
Themes are
concept
clusters
that
represent the most
semantically
connected groups
of concepts.
Theme name is
the
most prominent
concept
in
the
cluster.
1.
Set your Theme size
scroll bar to 100%
-
to see
the
concept
(s
) with
the
highest level
of connectivity
–
these
will be the
most important
themes
that are
coming out from
your text
(Fig 6)
. There might be one theme, two
or
,
sometimes
,
more
–
they all are
worth paying close attention. You could explore relevance of
the
them
es and, as in
the
case of concepts,
have access to all quotes that are illustrative of the theme.
Things to note:
If you
‘
ve got more than one theme at 100%
resolution
,
l
ook for
the
concepts that are positioned
in
the
intersected area
(
s
)
–
they might give you some insights of what
topics
‘
belong
’
to both
themes.
8
Fig
.
7
.
2.
When
reducing
percentage
on
the
scroll bar
,
more
themes (concept clusters) will
appear
on
the
map
(Fig.8)
.
Look carefully at
the
dyn
amic
s
of transformation
-
e.g.
how smaller themes merge into bigger ones,
what
themes
disappear
and what
remain.
3.
R
emember or better save the themes generated by the
automatic
analysis
. It is
possible that when you
made
some
custom
configuration
changes
,
the
thematic
picture
will also
change.
Fig.8
9
N
ext step
…
The n
ext
step
is making
necessary
adjustments
for a more
focused
/
customized
analysis
,
for
example
,
removing
concepts that are
not central for your research
,
merging or adding the
concepts,
activating sentiment analysis
,
adding
tagging
etc.
When
all
the
changes you think might be useful
for your research
are
done
,
the
analysis
should be run once more, following the same
stages that were
indicated/described above.
7.
Sentiment
analysis
Activating
the
sentiment
lens
can
be a bit tricky, so below is
a quick
step by step
guide how to do it.
1.
To enable sentiment analysis, in the Project Control Panel
select
‘
Generate
Thesaurus
’
and then activate
‘
Show Settings
’
.
2.
Click on
Concept Seeds
’
and
then
on
‘
Edit
’
(Fig
9
).
3.
Choose
User Defined Concepts
from the top menu
and
then
click on
Sentiment
Lens
button that
is
activated
/highlighted
.
Favourable
(favterms) and
Unfavourable
(unfavterms) terms will appear under the
Concept list
(Fig
10
)
.
4.
S
ave
the
settings by pressing
‘
ok
’
and then run your analysis from scratch so that
s
entiment analysis is
incorporated
.
Fig.
9
10
Fig.
10
Why
use
the
sentiment analysis
?
Sentiment analysis
gives you an instant indication of
probability
of a
concept
being mentioned in a favourable and
unfavourable
con
text
.
J
ust
click
on a concept in the
Concept Ranking Table
and see
the
results
.
T
he difference could be
relatively small
(as in Fig
11
–
just 1%
or under
) or quite
noticeable
.
The latter case will be an
evidence of a
particular
strong
disposition
of your research
participants
.
By clicking on
favourable
/
unfavourable
you will have access to
all
quotes that were selected as indicative of
the
particular sentiment
for this concept.
Things to note:
Don
’
t be surprised if
,
for example
,
the
percentage of
unfavourable
likelihood
is
higher, but number of quotes selected by
the
software
will
be
the same
than in case of
favourable
quotes. This
is not a direct/
proportional
relationship
,
as
it is
determined by
various
statistical procedures and indicative
of more complex relationships
between
the
concept
and strength of the sentiments expressed.
Things to n
ote
:
Pay attention to the location of
favourably
and
unfavourably
rated
concepts on
the
map.
Sometimes
the
same
sentiment
concept
s
cluster together
,
this would be an indication of a
strong
shared concern
s
/issue
s
.
It is also useful to explore themes in
relation to dominant sentiment background of
the
concept that form
the theme.
11
Fig
11
.
Final remarks
You could
use
some
advanced
function
s
useful
for the analysis
-
for
example t
agging
f
iles and groups of file
s
to
allow
for
comparison
,
c
reat
ing
D
ashboard Report
s
, explore word cloud
, look at your concept
map from
the
‘
social
network’
perspective etc
.
I
nstructions how to do it
can
be found in
the
‘
Help
’
section of
the
software.
Some advance
features
are
also
explained in
https://www.leximancer.com/dl/training/2011
-
12
-
V4194059105
-
AB
-
45
-
J/Leximanc
erIn
-
depth.ppt
The m
ain
and most time consuming
work will be about
delv
ing
into the
actual quotes
and
extract
ing
meaning
s of
the
concepts and concept
connections
, making sense of
themes and general landscape of your
text.
When you
have
had a chance to compare
the outcomes of
a
‘fully
automated’ analysis
and
a
researcher
-
driven
,
customized
one
you
might decide that automated analysis
is
more insightful. It is
easier
to
start everything from scratch (as a new project)
rather
than trying to
‘
undo
’
the changes that were made.
Comparative analysis of concept maps
Leximancer could be
a helpful tool when you need to compare two
data sets or to look at the data
longitudinally
.
Look for
new, emerging
concepts
and concepts that
have
disappeared
,
explore
how
the
relevance of
the
concepts and
themes
have
changed,
what
happened
to direction and
strength
of
the
sentiments for the key concept, how
positioning of
the
main concepts and
their
neighbours changed.
Comparative analysis could be
especially
insightful!
12
Please send your questions or comments
related
to
this guid
e
to
Dr
E
lena
Z
aitseva (
e.zaitseva
@
ljmu.ac.uk
)
7.
References
1.
Smith, A.
&
Humpreys
.
M.
,
2006
,
‘
Evaluation of unsupervised semantic mapping
of
natural language with Leximancer concept mapping
’
,
Behavioural Research
Methods
,
(
38
), pp.
262
–
79.
2.
Dodgson
et al
., 2008
‘
Content analysis of submissions by
Leximancer
’
. Available at
http://www.innovation.gov.au/Innovation/Policy/Documents/LeximancerSubmissionA
nalysis.pdf
(accessed
15
April 2013
)
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