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Su
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co
m
m
it
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wo
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lead
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s
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1
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s
[
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Su
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ates
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lace
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with
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u
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r
ates
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ld
wid
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[
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d
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4
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t
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a
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a
k
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o
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e
[
6
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.
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ag
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s
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ad
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lts
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m
ity
[
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.
Similar
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elicited
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[
9
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also
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m
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es
[
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ev
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[
5
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s
tr
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lan
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[
1
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T
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at
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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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s
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Tw
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(
X
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Mu
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r
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1315
am
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1
1
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is
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in
g
in
cr
ea
s
in
g
ly
co
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m
o
n
[
1
2
]
,
[
1
3
]
.
E
ar
l
y
in
d
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r
s
o
f
s
u
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id
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d
etec
ted
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r
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u
g
h
s
en
tim
en
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aly
s
is
[
5
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,
wh
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h
ca
n
also
r
ec
o
r
d
u
s
er
em
o
tio
n
s
[
1
4
]
.
Ho
wev
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n
eith
er
p
s
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ts
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o
r
p
s
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ts
en
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o
r
s
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th
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s
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ies as v
alid
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s
o
f
th
e
f
in
d
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s
.
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ased
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ased
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h
es
a
r
e
two
o
f
th
e
m
o
s
t
p
o
p
u
lar
m
et
h
o
d
s
.
E
ac
h
s
tr
ateg
y
h
as
b
en
ef
its
an
d
d
r
awb
ac
k
s
o
f
its
o
wn
.
T
h
e
lex
ico
n
-
b
ased
ap
p
r
o
ac
h
m
atc
h
es
wo
r
d
s
in
a
tex
t
with
a
lis
t
o
f
s
en
tim
en
t
wo
r
d
s
to
d
eter
m
in
e
wh
eth
er
a
tex
t
is
p
o
s
itiv
e,
n
eg
ativ
e,
o
r
n
e
u
tr
al.
Ho
wev
er
,
th
e
m
ac
h
in
e
lear
n
i
n
g
m
eth
o
d
r
elies
o
n
a
d
atase
t
lab
eled
with
a
p
ar
tic
u
lar
s
en
tim
en
t
to
tr
ain
a
m
o
d
el.
W
h
er
ea
s
th
e
m
ac
h
in
e
lear
n
in
g
ap
p
r
o
ac
h
w
o
r
k
s
b
est
with
lar
g
e
d
ata,
th
e
lex
ico
n
a
p
p
r
o
ac
h
wo
r
k
s
b
est
with
s
m
aller
d
ata
[
1
5
]
.
T
h
is
s
tu
d
y
u
s
es
a
h
y
b
r
id
a
p
p
r
o
ac
h
o
f
le
x
ico
n
-
b
ased
s
en
ti
m
en
t
p
o
la
r
izatio
n
a
n
d
m
ac
h
i
n
e
lear
n
in
g
.
Sen
tim
en
t
p
o
lar
izatio
n
is
th
e
p
r
ac
tice
o
f
class
if
y
in
g
em
o
tio
n
s
co
n
v
ey
ed
i
n
tex
t
d
ata
in
to
d
is
tin
ct
p
o
lar
ities
(
u
s
u
ally
p
o
s
itiv
e,
n
eg
ativ
e
,
a
n
d
n
e
u
tr
al)
,
u
s
in
g
lex
ico
n
-
b
ased
with
v
ale
n
ce
awa
r
e
d
ictio
n
ar
y
a
n
d
s
en
tim
en
t
r
ea
s
o
n
er
(
VADE
R
)
,
I
n
d
o
n
esia
n
s
en
tim
en
t
(
I
NSET
)
,
an
d
v
ali
d
ated
b
y
p
s
y
ch
o
lo
g
is
ts
.
I
NSET
p
er
f
o
r
m
s
well
as
an
I
n
d
o
n
esian
em
o
tio
n
le
x
ic
o
n
in
p
r
ed
ictin
g
b
r
ie
f
wr
itte
n
th
o
u
g
h
ts
’
n
e
g
ativ
e
an
d
p
o
s
itiv
e
p
o
lar
ity
[
1
6
]
.
VADE
R
is
a
r
u
le
-
an
d
lex
ic
o
n
-
b
ased
s
en
tim
en
t
an
aly
s
is
to
o
l
p
r
ec
is
ely
alig
n
ed
with
th
e
s
en
tim
en
t
ex
p
r
ess
ed
o
n
s
o
cial
m
ed
ia
[
1
7
]
.
T
h
e
class
if
icatio
n
m
o
d
el
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
u
s
es th
e
Naïv
e
B
ay
es tec
h
n
iq
u
e,
K
-
n
ea
r
est
n
eig
h
b
o
r
(
KNN)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SV
M)
.
Naïv
e
B
ay
es
is
a
s
h
o
r
t
alg
o
r
ith
m
b
ased
o
n
B
ay
es
’
th
eo
r
em
f
o
r
co
n
d
itio
n
al
p
r
o
b
a
b
ilit
y
.
T
h
e
Naïv
e
B
ay
es
alg
o
r
ith
m
ass
u
m
es
th
at
all
d
ata
is
in
d
ep
en
d
e
n
t.
T
h
e
m
eth
o
d
is
ass
u
m
ed
to
b
e
ca
p
ab
le
o
f
d
etec
tin
g
th
e
d
e
p
en
d
en
ce
o
n
th
e
tr
ai
n
in
g
f
ea
tu
r
es
[
1
7
]
.
A
SVM
is
ess
en
tial
f
o
r
an
in
teg
r
ated
a
n
d
s
u
p
er
v
is
ed
class
if
icatio
n
s
tr
ateg
y
s
in
ce
th
e
tr
ain
in
g
p
r
o
ce
s
s
r
eq
u
ir
es
s
p
ec
if
ic
lear
n
in
g
tar
g
ets
[
1
8
]
.
KNN
is
a
m
eth
o
d
f
o
r
class
if
y
in
g
o
b
je
cts
b
ased
o
n
th
eir
p
r
o
x
im
ity
t
o
th
e
item
.
Desp
ite
its
s
im
p
licity
,
KNN
i
s
h
ig
h
ly
g
o
o
d
f
o
r
ca
te
g
o
r
izatio
n
[
1
9
]
.
Su
icid
e
an
d
o
n
lin
e
b
e
h
av
io
r
r
e
s
ea
r
ch
h
as b
ee
n
u
n
d
e
r
tak
en
in
v
ar
io
u
s
co
u
n
tr
ies,
in
clu
d
in
g
J
a
p
an
[
1
0
]
,
T
aiwa
n
[
2
0
]
,
Am
er
ica
[
2
1
]
,
an
d
Au
s
tr
alia
[
2
2
]
.
Similar
s
tu
d
ies
em
p
lo
y
in
g
I
n
d
o
n
esian
id
io
m
s
ar
e
s
till
r
eg
ar
d
e
d
as
e
x
tr
em
ely
r
ar
e
in
I
n
d
o
n
esia.
Fu
r
th
er
m
o
r
e,
b
ec
a
u
s
e
th
ese
id
io
m
s
c
h
an
g
e
o
v
er
ti
m
e,
m
o
r
e
r
esear
ch
is
r
eq
u
ir
ed
to
f
u
lly
u
n
d
e
r
s
tan
d
th
em
an
d
u
s
e
th
em
to
p
r
ev
en
t
s
u
icid
al
th
o
u
g
h
ts
as
ea
r
ly
as
p
o
s
s
ib
le.
Su
ch
ex
p
r
ess
io
n
s
ca
n
o
n
l
y
b
e
o
b
tain
ed
f
r
o
m
s
o
cial
m
ed
ia
p
latf
o
r
m
s
lik
e
X.
T
h
is
s
tu
d
y
ex
a
m
in
es
s
u
icid
al
attitu
d
es
o
n
th
e
s
o
cial
m
ed
ia
p
latf
o
r
m
X
in
I
n
d
o
n
esia.
Un
d
er
s
tan
d
in
g
X
u
s
er
s
’
f
ee
lin
g
s
ab
o
u
t
s
u
icid
e
m
ig
h
t
p
r
o
v
i
d
e
s
ig
n
if
ican
t
in
s
ig
h
t
in
to
p
u
b
lic
p
er
ce
p
tio
n
s
,
p
o
ten
tial
r
is
k
f
ac
to
r
s
,
an
d
in
ter
v
e
n
tio
n
o
p
p
o
r
tu
n
ities
.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
s
tu
d
y
is
to
co
n
tr
i
b
u
te
to
b
r
o
a
d
er
ef
f
o
r
ts
in
m
en
tal
h
ea
lth
awa
r
en
ess
an
d
s
u
icid
e
p
r
ev
en
tio
n
in
I
n
d
o
n
esia
b
y
an
aly
zin
g
th
e
s
en
tim
en
t
o
f
s
u
icid
e
-
r
elate
d
X
m
ess
ag
es.
2.
M
E
T
H
O
D
T
o
estab
lis
h
a
v
iab
le
m
eth
o
d
o
lo
g
y
to
m
o
n
ito
r
s
u
icid
e
id
ea
ti
o
n
,
a
m
eth
o
d
ical
s
tr
ateg
y
f
o
r
g
ath
er
in
g
,
ev
alu
atin
g
,
a
n
d
in
ter
p
r
etin
g
o
n
lin
e
d
is
cu
s
s
io
n
s
is
n
ec
ess
ar
y
.
T
h
e
s
tep
s
lis
ted
b
elo
w
will b
e
tak
en
:
a.
A
p
r
elim
in
ar
y
i
n
v
esti
g
atio
n
w
as
co
n
d
u
cted
t
o
id
e
n
tify
ter
m
s
ty
p
ically
ass
o
ciate
d
with
s
u
i
cid
e
id
ea
tio
n
.
T
h
e
p
r
o
ce
s
s
o
f
f
in
d
i
n
g
k
e
y
w
o
r
d
s
n
ev
e
r
en
d
s
.
T
h
e
m
o
r
e
i
n
-
d
ep
th
th
e
s
tu
d
y
,
th
e
m
o
r
e
l
ik
ely
it
is
th
at
m
o
r
e
ter
m
s
r
elate
d
to
s
u
icid
al
th
o
u
g
h
ts
will
b
e
f
o
u
n
d
.
L
o
o
k
o
v
er
th
e
k
ey
wo
r
d
lis
t
an
d
elim
in
ate
an
y
to
o
b
r
o
ad
,
to
o
s
p
ec
if
ic,
o
r
less
r
elev
an
t.
B
ased
o
n
th
eir
u
s
ef
u
ln
ess
,
f
r
eq
u
en
cy
o
f
u
s
e,
an
d
p
o
te
n
t
ial
im
p
ac
t o
n
s
u
icid
al
d
ata,
ce
r
tain
k
ey
wo
r
d
s
ar
e
m
o
r
e
im
p
o
r
ta
n
t.
T
h
e
o
u
tco
m
e
o
f
th
e
p
r
elim
in
ar
y
in
v
e
s
tig
atio
n
is
a
lis
t o
f
s
u
g
g
ested
cr
awla
b
le
k
e
y
wo
r
d
s
.
b.
C
r
awlin
g
an
d
s
cr
ap
in
g
in
f
o
r
m
atio
n
f
r
o
m
X
u
s
in
g
p
r
o
p
o
s
ed
k
ey
wo
r
d
s
r
elate
d
to
s
u
icid
al
id
e
atio
n
.
c.
Data
p
r
ep
r
o
ce
s
s
in
g
:
d
ata
m
u
s
t
b
e
clea
n
ed
,
f
o
l
d
ed
in
t
o
ca
s
es,
to
k
en
ized
,
f
ilter
ed
,
an
d
s
tem
m
ed
b
ef
o
r
e
it
ca
n
b
e
an
aly
ze
d
.
C
lean
in
g
th
e
d
ata
to
r
em
o
v
e
an
y
u
n
n
e
ce
s
s
ar
y
ch
ar
ac
ter
s
,
s
y
m
b
o
ls
,
an
d
n
o
n
-
tex
t
o
b
jects.
C
ase
f
o
ld
in
g
ch
an
g
es
all
tex
t
to
lo
wer
ca
s
e
to
f
ac
ilit
a
te
u
n
if
o
r
m
c
o
m
p
ar
is
o
n
s
.
T
o
to
k
en
ize
a
tex
t
m
ea
n
s
to
d
iv
id
e
it
in
to
to
k
en
s
o
r
in
d
iv
id
u
al
wo
r
d
s
.
C
o
m
m
o
n
ter
m
s
(
s
to
p
wo
r
d
s
)
s
u
ch
a
s
‘
an
d
’
,
‘
th
e
’
,
‘
is
’
,
an
d
s
o
o
n
ar
e
d
elete
d
d
u
r
in
g
f
ilter
in
g
s
in
ce
th
ey
ty
p
ical
ly
d
o
n
o
t
p
r
o
v
i
d
e
u
s
ef
u
l
in
f
o
r
m
atio
n
to
th
e
s
tu
d
y
.
No
r
m
aliza
tio
n
is
co
n
v
e
r
tin
g
in
co
m
p
lete
wo
r
d
s
,
m
is
ta
k
es,
an
d
ty
p
in
g
er
r
o
r
s
in
to
w
o
r
d
s
in
th
e
b
ig
in
d
o
n
esian
d
ictio
n
ar
y
(
KB
B
I
)
o
r
b
y
I
n
d
o
n
esian
en
h
a
n
ce
d
s
p
ellin
g
(
E
YD)
.
W
h
ile
lem
m
atiz
atio
n
ch
an
g
es
wo
r
d
s
to
th
eir
b
ase
f
o
r
m
(
f
o
r
ex
am
p
le,
‘
b
etter
’
b
ec
o
m
es
‘
g
o
o
d
’
)
,
s
tem
m
in
g
c
h
an
g
es
wo
r
d
s
to
th
eir
o
r
ig
in
f
o
r
m
(
f
o
r
ex
am
p
le,
‘
r
u
n
n
in
g
’
b
ec
o
m
es
‘
r
u
n
’
)
.
I
t
h
el
p
s
to
r
ed
u
ce
wo
r
d
v
ar
iatio
n
s
to
th
eir
m
o
s
t
b
asic f
o
r
m
.
d.
Sen
tim
en
t
p
o
lar
izatio
n
:
t
o
asc
er
tain
wh
eth
er
a
s
en
tim
en
t
is
p
o
s
itiv
e,
n
eg
ativ
e,
o
r
n
e
u
tr
al,
VADE
R
an
d
I
NSET
ar
e
u
s
ed
to
p
o
lar
ize
it.
e.
Vis
u
aliza
tio
n
an
d
an
aly
s
is
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
5
,
Octo
b
e
r
20
25
:
1
3
1
4
-
1
3
2
2
1316
f.
C
o
n
d
u
ct
s
en
tim
en
t
an
aly
s
is
to
d
eter
m
i
n
e
th
e
attitu
d
e
o
f
t
h
e
co
n
ten
t
(
p
o
s
itiv
e,
n
eg
ativ
e
,
o
r
n
e
u
tr
al)
.
I
t
p
r
o
v
id
es in
s
ig
h
ts
in
to
p
u
b
lic
b
elief
s
ab
o
u
t su
icid
al
th
o
u
g
h
ts
.
g.
W
o
r
d
clo
u
d
s
ar
e
u
s
ed
to
g
r
ap
h
ically
r
ep
r
esen
t
th
e
ter
m
s
th
at
ap
p
ea
r
th
e
m
o
s
t
f
r
e
q
u
en
tly
in
th
e
d
ata.
I
t
co
n
cisely
r
ev
iews th
e
m
o
s
t d
is
cu
s
s
ed
to
p
ics,
in
clu
d
in
g
an
y
p
o
s
itiv
e
o
r
n
eg
ativ
e
b
ac
k
g
r
o
u
n
d
.
h.
T
h
en
,
u
s
in
g
1
0
-
f
o
ld
cr
o
s
s
-
v
a
lid
atio
n
,
f
ea
tu
r
e
ex
tr
ac
tio
n
is
p
er
f
o
r
m
e
d
u
s
in
g
ter
m
f
r
eq
u
en
cy
-
in
v
e
r
s
e
d
o
cu
m
e
n
t
f
r
eq
u
e
n
cy
(
TF
-
I
DF)
to
ev
alu
ate
th
e
ass
o
ciatio
n
b
etwe
en
a
wo
r
d
an
d
a
d
o
c
u
m
en
t
an
d
to
ass
ess
th
e
ef
f
ec
tiv
en
ess
o
f
t
h
e
m
o
d
el
o
r
alg
o
r
ith
m
.
1
0
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
is
a
g
o
o
d
m
o
d
el
in
g
tech
n
iq
u
e
b
ec
au
s
e
its
ac
cu
r
ac
y
f
in
d
in
g
s
ar
e
less
b
iased
th
an
o
th
er
tec
h
n
iq
u
es
[
2
3
]
.
i.
T
h
e
Naïv
e
B
ay
es
tech
n
iq
u
e,
KNN,
an
d
SVM
ar
e
u
s
ed
in
th
e
class
if
icatio
n
m
o
d
el
b
ased
o
n
m
ac
h
i
n
e
lear
n
in
g
s
en
tim
en
t
p
o
lar
izatio
n
.
j.
T
h
e
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
a
ll,
an
d
F
1
-
s
co
r
e
o
f
t
h
e
alg
o
r
i
th
m
will
b
e
p
r
esen
ted
.
T
h
e
p
er
ce
n
tag
e
o
f
co
r
r
ec
t
p
r
ed
ictio
n
s
b
ased
o
n
t
h
e
wh
o
le
d
ata
is
ca
lled
ac
cu
r
ac
y
.
Pre
cisi
o
n
is
d
ef
in
ed
as
th
e
p
r
o
p
o
r
tio
n
o
f
co
r
r
ec
t
p
o
s
itiv
e
ca
lcu
latio
n
s
t
o
to
tal
p
o
s
itiv
e
ca
lcu
latio
n
s
.
T
h
e
f
r
ac
tio
n
o
f
co
r
r
ec
t
p
o
s
itiv
e
co
m
p
u
tatio
n
s
v
er
s
u
s
all
co
r
r
ec
t p
o
s
itiv
e
d
ata
is
r
ef
er
r
ed
to
as r
ec
all.
Fin
ally
,
th
e
F
1
-
s
co
r
e
co
n
tr
asts
p
r
ec
is
io
n
an
d
r
ec
al
l
weig
h
ted
av
er
a
g
es.
Fig
u
r
e
1
d
e
p
icts
a
s
u
m
m
ar
y
o
f
th
e
s
tag
es o
f
th
is
r
esear
ch
.
Fig
u
r
e
1
.
Sy
n
o
p
s
is
o
f
r
esear
c
h
s
tag
es
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
3
.
1
.
Da
t
a
c
o
llect
io
n
T
h
e
lack
o
f
p
u
b
licly
av
ailab
le
d
ata
o
n
s
u
icid
e
in
I
n
d
o
n
esia
is
a
b
ar
r
ier
.
Ho
wev
er
,
with
an
in
cr
ea
s
in
g
n
u
m
b
er
o
f
p
eo
p
le
r
elea
s
in
g
t
h
eir
f
ee
lin
g
s
o
n
s
o
cial
m
ed
ia,
it
ca
n
b
e
u
s
ed
to
co
llect
d
ata
o
n
th
e
s
u
icid
e
is
s
u
e.
X
is
an
ex
ce
llen
t p
r
im
ar
y
s
o
u
r
ce
f
o
r
in
f
o
r
m
atio
n
ab
o
u
t m
en
t
al
illn
ess
es,
in
clu
d
in
g
s
u
icid
e
[
1
0
]
,
[
1
8
]
.
Fin
d
in
g
k
ey
wo
r
d
s
r
elate
d
to
s
u
icid
e
is
n
o
t
a
s
im
p
le
ta
s
k
.
Se
ar
ch
ter
m
s
f
o
r
s
u
icid
e
in
Go
o
g
le
T
r
en
d
s
s
h
o
w
‘
co
m
m
it
s
u
icid
e,
’
‘
h
o
w
to
co
m
m
it
s
u
icid
e,
’
an
d
‘
s
u
icid
e
p
r
ev
e
n
tio
n
’
[
1
9
]
,
[
2
0
]
.
T
h
e
u
s
e
o
f
th
e
id
io
m
s
‘
wan
t
to
co
m
m
it
s
u
icid
e
’
an
d
‘
wan
t
to
d
ie
’
ar
e
p
o
s
ts
th
a
t
co
r
r
elate
with
s
u
icid
al
id
ea
t
io
n
[
2
4
]
.
Ho
wev
er
,
wan
tin
g
to
co
m
m
it su
icid
e
s
h
o
ws m
o
r
e
s
er
io
u
s
n
ess
o
f
s
u
icid
al
in
ten
tio
n
s
[
1
0
]
.
L
o
o
k
in
g
at
th
e
s
p
ec
if
ic
s
itu
atio
n
in
I
n
d
o
n
esia,
th
e
k
e
y
wo
r
d
s
s
elec
ted
f
o
r
cr
awlin
g
X
d
ata
in
clu
d
e
‘
s
u
icid
e
’
,
‘
wis
h
to
d
ie
’
,
a
n
d
‘
wan
t
to
s
u
icid
e
’
.
Data
was
co
llected
f
o
r
f
o
u
r
(
4
)
m
o
n
th
s
,
with
a
to
tal
o
f
5
,
5
3
5
twee
ts
co
n
tain
in
g
d
etails o
f
s
u
icid
e
(
4
6
3
4
)
,
wis
h
to
d
ie
(
9
0
0
)
,
an
d
wan
t t
o
s
u
icid
e
(
1
0
0
)
.
Data
clea
n
in
g
r
esu
lts
in
clu
d
e
ca
s
e
f
o
ld
in
g
(
co
n
v
er
tin
g
d
ata
to
lo
wer
ca
s
e
an
d
clea
n
in
g
tex
t)
,
to
k
en
izin
g
(
cu
ttin
g
s
tr
in
g
s
b
ased
o
n
th
e
co
n
s
titu
en
t
wo
r
d
s
)
,
n
o
r
m
aliza
tio
n
(
co
r
r
ec
tio
n
o
f
in
co
m
p
lete
wo
r
d
s
an
d
ty
p
in
g
er
r
o
r
s
ad
ju
s
ted
f
o
r
E
YD
–
en
h
a
n
ce
d
s
p
ellin
g
)
,
r
em
o
v
in
g
s
to
p
w
o
r
d
s
,
s
tem
m
i
n
g
(
r
etr
iev
in
g
b
asic
wo
r
d
s
)
an
d
elim
in
atin
g
d
u
p
lic
atio
n
.
Fin
ally
,
we
co
llected
a
t
o
tal
o
f
2
.
4
2
5
twee
t d
ata.
Evaluation Warning : The document was created with Spire.PDF for Python.
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ased
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ased
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ata
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ile
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ad
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ata
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NSET
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ata
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ep
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lab
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e
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Fig
u
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e
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)
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Fig
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ile
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s
am
e
(
eq
u
ally
p
r
e
v
alen
t
in
b
o
th
p
o
s
itiv
e
an
d
n
eg
ativ
e
wo
r
d
clo
u
d
s
)
,
th
e
d
ictio
n
th
at
g
o
es
with
th
em
d
if
f
er
s
.
T
h
e
p
h
r
asin
g
ac
co
m
p
an
y
in
g
it
in
th
e
n
eg
ativ
e
wo
r
d
clo
u
d
is
wis
h
in
g
to
d
ie;
h
o
wev
er
,
in
th
e
p
o
s
itiv
e
wo
r
d
clo
u
d
,
th
e
d
ictio
n
ac
co
m
p
an
y
in
g
it is
Allah
as a
n
in
ce
n
tiv
e
to
li
v
e.
(
a)
(
b
)
Fig
u
r
e
2
.
W
o
r
d
clo
u
d
p
o
s
itiv
e
an
d
n
e
g
ativ
e
s
en
tim
en
t: (
a)
p
o
s
itiv
e
wo
r
d
clo
u
d
an
d
(
b
)
n
eg
a
tiv
e
wo
r
d
clo
u
d
3.
3
.
Cla
s
s
if
ica
t
io
n per
f
o
rm
a
nce
T
h
e
clea
n
ed
d
ata
is
th
en
m
o
d
eled
with
Naïv
e
B
ay
es,
S
VM
,
an
d
KNN.
T
h
e
r
esu
lts
s
h
o
w
th
at
SVM
(
8
7
.
6
5
%)
h
as th
e
h
ig
h
est lev
el
o
f
ac
cu
r
ac
y
,
f
o
llo
we
d
b
y
KNN
(
8
6
.
8
3
%)
an
d
Naïv
e
B
ay
es (
8
6
.
2
1
%).
T
h
e
h
ig
h
lev
el
o
f
ac
cu
r
ac
y
d
em
o
n
s
tr
ate
s
th
at
th
is
ap
p
r
o
ac
h
ca
n
p
r
o
v
i
d
e
im
p
o
r
tan
t
in
s
ig
h
t
in
to
s
u
ic
id
e
o
n
X.
Fig
u
r
e
4
s
h
o
ws
m
o
r
e
co
m
p
r
e
h
en
s
iv
e
r
esu
lts
f
r
o
m
th
ese
th
r
ee
alg
o
r
i
th
m
s
:
Fig
u
r
e
4
(
a)
SVM,
Fig
u
r
e
4
(
b
)
KNN,
an
d
Fig
u
r
e
4
(
c)
Naïv
e
B
ay
es.
Fig
u
r
e
5
d
ep
icts
th
e
m
o
d
el
ev
al
u
atio
n
f
in
d
in
g
s
,
wh
er
e
th
is
m
atr
ix
co
m
p
a
r
es
th
e
class
if
icatio
n
r
esu
lts
ac
h
iev
ed
b
y
th
e
m
o
d
el
with
th
e
ac
t
u
al
c
lass
if
icatio
n
r
esu
lts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
5
,
Octo
b
e
r
20
25
:
1
3
1
4
-
1
3
2
2
1318
(
a)
(
b
)
Fig
u
r
e
3
.
W
o
r
d
s
th
at
o
f
ten
a
p
p
ea
r
in
(
a)
p
o
s
itiv
e
an
d
(
b
)
n
eg
a
tiv
e
s
en
tim
en
ts
(
a)
(
b
)
(
c)
Fig
u
r
e
4
.
Mo
d
el
test
in
g
r
esu
lts
: (
a)
SVM,
(
b
)
KNN,
an
d
(
c)
Naïv
e
B
ay
es
Fig
u
r
e
5
.
Mo
d
el
ev
alu
atio
n
r
e
s
u
lts
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Lexico
n
-
b
a
s
ed
c
o
mp
a
r
is
o
n
fo
r
s
u
icid
e
s
en
timen
t a
n
a
lysi
s
o
n
Tw
itter
(
X
)
(
Mu
n
a
w
a
r
)
1319
3.
4
.
Dis
cu
s
s
io
n
Su
icid
e
p
r
ev
en
tio
n
r
eq
u
ir
es
ea
r
ly
d
etec
tio
n
a
n
d
ac
tio
n
.
Alth
o
u
g
h
s
ea
r
c
h
en
g
i
n
es
lik
e
Go
o
g
le
T
r
en
d
s
ca
n
ass
is
t
in
m
ap
p
in
g
s
u
icid
e
tr
en
d
s
in
a
g
iv
e
n
lo
ca
tio
n
[
1
9
]
,
[
2
5
]
,
th
ey
ca
n
n
o
t
id
en
tif
y
wh
o
h
as
s
u
icid
al
in
ten
t.
T
h
e
in
cr
ea
s
in
g
ly
wid
e
s
p
r
ea
d
ex
p
r
ess
io
n
o
f
em
o
tio
n
o
n
s
o
cial
m
ed
ia,
p
ar
ticu
lar
ly
X,
ca
n
aid
in
th
e
ea
r
ly
d
etec
tio
n
o
f
s
u
icid
e,
p
ar
ticu
lar
ly
am
o
n
g
y
o
u
th
.
As
tim
e
p
ass
es,
id
io
m
s
ab
o
u
t
s
u
icid
e,
p
ar
ticu
lar
ly
am
o
n
g
y
o
u
th
,
e
v
o
lv
e.
Usi
n
g
t
h
e
k
ey
wo
r
d
s
‘
s
u
icid
e
’
,
‘
wis
h
to
d
ie
’
,
an
d
‘
wa
n
t
to
co
m
m
it
s
u
icid
e
’
is
h
ig
h
ly
ef
f
ec
tiv
e
in
co
llectin
g
s
u
icid
e
d
ata
f
r
o
m
X.
T
h
e
u
s
e
o
f
a
lex
ic
o
n
g
r
ea
tly
aid
s
lab
elin
g
.
I
t
is
m
er
ely
t
h
at
b
ec
au
s
e
it
is
r
elate
d
to
s
u
icid
e,
a
p
s
y
ch
o
lo
g
is
t m
u
s
t v
alid
ate
th
e
s
en
ten
ce
s
ac
q
u
ir
ed
.
VADE
R
is
m
o
r
e
ac
cu
r
ate
in
th
e
s
u
icid
e
co
n
tex
t th
an
in
t
h
e
I
NSET
.
Fu
r
th
er
r
esear
ch
o
n
o
th
er
is
s
u
es
is
r
eq
u
ir
ed
to
o
b
tain
r
ec
o
m
m
en
d
atio
n
s
f
o
r
em
p
lo
y
in
g
a
m
o
r
e
co
m
p
r
eh
e
n
s
iv
e
I
n
d
o
n
esian
lex
ico
n
th
at
m
ay
b
e
u
s
ed
wid
el
y
.
Acc
o
r
d
in
g
to
th
e
s
en
tim
e
n
t
a
n
aly
s
is
r
esu
lts
,
th
e
g
ath
er
ed
t
wee
t
co
n
ten
t
in
clu
d
ed
8
6
.
4
%
n
eg
ativ
e,
1
1
.
1
%
p
o
s
itiv
e,
an
d
2
.
5
%
n
e
u
tr
al
co
n
ten
t.
I
t
d
em
o
n
s
tr
ates
th
e
s
er
io
u
s
n
ess
o
f
th
e
s
u
icid
al
co
n
ten
t.
Po
s
itiv
e
co
n
ten
t,
wh
ich
ac
co
u
n
ts
f
o
r
o
n
ly
1
1
.
1
%,
in
d
icate
s
th
at
o
th
e
r
X
u
s
er
s
en
co
u
r
ag
e
co
n
ten
t
u
p
lo
ad
er
s
to
r
e
m
ai
n
p
ass
io
n
ate
ab
o
u
t
life
a
n
d
r
em
em
b
er
Allah
.
Giv
en
th
e
h
ar
m
f
u
l
im
p
ac
t
o
f
o
p
en
n
ess
in
th
e
ac
tu
al
wo
r
ld
,
it
is
ar
g
u
ed
t
h
at
d
ec
is
io
n
-
m
a
k
er
s
s
h
o
u
ld
m
a
k
e
a
m
o
r
e
s
er
io
u
s
ef
f
o
r
t to
b
u
ild
ch
an
n
els to
s
u
p
p
o
r
t p
er
s
o
n
s
wh
o
h
a
v
e
s
u
icid
al
th
o
u
g
h
ts
.
T
h
e
h
ig
h
f
r
e
q
u
en
c
y
o
f
s
p
ec
i
f
ic
ter
m
s
in
twee
ts
u
s
er
s
s
en
d
in
d
icate
s
th
at
th
ese
wo
r
d
s
ca
n
s
p
ar
k
s
u
icid
al
th
o
u
g
h
ts
.
Su
icid
al
in
ten
t
ca
n
b
e
s
h
o
wn
b
y
u
s
in
g
s
u
icid
e
an
d
d
esirin
g
to
d
ie
in
a
n
eg
ativ
e
co
n
tex
t.
Usi
n
g
s
u
icid
e
with
life
an
d
Allah
,
o
n
th
e
o
th
er
h
a
n
d
,
ca
n
co
n
v
ey
m
o
tiv
atio
n
to
s
tay
al
iv
e
n
o
m
atter
h
o
w
ch
allen
g
in
g
th
e
p
r
o
b
lem
s
ar
e
.
On
th
e
o
th
er
h
an
d
,
t
h
is
in
d
icato
r
co
u
ld
im
p
ly
s
u
icid
e
id
ea
tio
n
f
r
o
m
p
u
b
lic
v
iew,
allo
win
g
f
o
r
ea
r
ly
i
n
ter
v
en
tio
n
.
T
h
er
e
ar
e
s
ev
er
al
m
eth
o
d
s
f
o
r
s
p
o
ttin
g
s
u
icid
al
war
n
in
g
s
i
g
n
s
o
n
s
o
cial
m
ed
ia.
Fro
m
th
e
co
llected
d
ata,
s
o
m
e
co
n
cl
u
s
io
n
s
ca
n
b
e
m
ad
e,
in
clu
d
in
g
t
h
e
f
o
llo
win
g
:
−
A
h
ig
h
d
e
g
r
ee
o
f
s
u
icid
al
in
te
n
t
is
in
d
icate
d
b
y
p
o
s
ts
th
at
u
tili
ze
th
e
ter
m
s
‘
s
u
icid
e
’
an
d
‘
wan
t
to
d
ie
’
.
T
h
is
ca
n
s
er
v
e
as a
n
ea
r
l
y
war
n
in
g
s
ig
n
f
o
r
s
u
icid
e
attem
p
t p
r
ev
en
tio
n
[
2
6
]
.
−
An
o
th
er
ea
r
ly
s
ig
n
o
f
s
u
ici
d
al
th
o
u
g
h
ts
is
t
h
e
u
s
ag
e
o
f
th
e
wo
r
d
‘
ca
p
ek
’
(
tire
d
)
wh
ich
co
n
v
ey
s
s
en
tim
en
ts
o
f
b
ein
g
a
b
u
r
d
en
(
f
o
r
in
s
tan
ce
,
as a
r
esu
lt
o
f
illn
ess
)
[
8
]
.
−
Per
s
is
ten
t s
tr
ess
o
r
d
ep
r
ess
io
n
m
ig
h
t e
x
ac
er
b
ate
s
u
icid
al
th
o
u
g
h
ts
[
2
7
]
.
−
Su
icid
al
th
o
u
g
h
ts
ca
n
b
e
a
p
r
ec
u
r
s
o
r
to
d
e
p
r
ess
io
n
an
d
f
e
elin
g
s
o
f
g
u
ilt
an
d
s
in
.
I
n
th
e
p
r
esen
ce
o
f
h
o
p
eless
n
ess
,
th
ese
s
u
icid
al
th
o
u
g
h
ts
will b
ec
o
m
e
s
u
icid
al
i
n
ten
tio
n
s
[
2
8
]
.
T
h
ese
r
esu
lts
ar
e
m
er
ely
p
r
elim
in
ar
y
in
d
icatio
n
s
.
I
t
h
as
to
b
e
in
v
esti
g
ated
f
u
r
th
er
to
s
er
v
e
as
a
p
r
ec
u
r
s
o
r
o
f
s
u
icid
e
in
ten
t
in
s
o
cial
m
ed
ia
p
o
s
ts
.
T
h
is
is
s
ig
n
if
ican
t
s
in
ce
ab
o
u
t
6
%
o
f
p
eo
p
le
wh
o
h
a
v
e
s
u
icid
al
th
o
u
g
h
ts
en
d
th
eir
liv
es
[
2
9
]
.
T
h
e
m
o
d
elin
g
r
esu
lts
with
SVM,
KNN,
an
d
Naïv
e
B
ay
es
all
s
h
o
w
ac
cu
r
ac
y
ab
o
v
e
8
6
%,
with
SVM
h
av
in
g
th
e
m
o
s
t
r
em
ar
k
ab
le
a
cc
u
r
ac
y
(
8
7
.
6
5
%).
T
h
is
h
ig
h
a
cc
u
r
ac
y
d
e
m
o
n
s
tr
ates
th
at
th
is
m
o
d
el
ca
n
i
d
en
tify
o
r
ass
ess
s
u
s
p
icio
u
s
b
eh
av
io
r
r
elate
d
to
s
u
icid
e
is
s
u
es
to
ca
r
r
y
o
u
t
p
r
ev
en
tio
n
an
d
ea
r
ly
in
ter
v
e
n
tio
n
.
Ho
wev
er
,
m
o
r
e
ef
f
o
r
ts
ar
e
r
eq
u
ir
ed
to
ad
d
r
ess
th
e
is
s
u
e
o
f
s
u
icid
e
u
s
in
g
an
ass
o
ciatio
n
r
u
l
e
m
in
in
g
tech
n
i
q
u
e
to
u
n
co
v
er
wo
r
d
s
th
at
f
r
e
q
u
e
n
tly
ap
p
ea
r
to
g
eth
e
r
ab
o
u
t
s
u
icid
e.
I
t
will
co
n
s
id
er
ab
ly
ai
d
in
cr
ea
tin
g
m
o
r
e
ef
f
ec
tiv
e
an
d
r
elev
an
t
p
r
ev
e
n
tativ
e
an
d
in
ter
v
en
tio
n
tech
n
iq
u
es.
3.
5
.
Co
m
pa
riso
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RE
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[
1
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2
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