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en
t
an
aly
s
is
o
n
T
am
il
r
e
v
iews
an
d
s
o
cial
m
e
d
ia
to
tailo
r
th
eir
p
r
o
d
u
cts
an
d
s
er
v
ice
s
,
r
e
s
u
ltin
g
in
elev
ated
cu
s
to
m
er
s
atis
f
ac
tio
n
.
Mo
r
eo
v
er
,
s
en
tim
en
t
an
aly
s
is
in
T
am
il
h
o
ld
s
co
n
s
id
er
ab
le
im
p
o
r
ta
n
ce
in
p
o
liti
ca
l
an
d
s
o
cial
r
ea
lm
s
,
p
r
o
v
id
in
g
p
o
lic
y
m
ak
er
s
an
d
r
esear
ch
er
s
with
in
v
alu
ab
le
in
s
ig
h
ts
in
to
p
u
b
lic
s
en
tim
en
t.
T
h
is
,
in
tu
r
n
,
co
n
tr
i
b
u
tes to
m
o
r
e
in
f
o
r
m
ed
an
d
in
s
ig
h
tf
u
l
d
ec
is
io
n
-
m
ak
in
g
p
r
o
ce
s
s
es [
5
]
.
T
h
e
r
esear
ch
er
s
h
av
e
p
r
esen
ted
a
v
ar
iety
o
f
m
et
h
o
d
o
lo
g
ies;
h
o
wev
er
,
th
e
ch
allen
g
es
p
er
s
is
t
d
u
e
to
th
e
s
h
o
r
tag
e
o
f
lab
ele
d
d
ata
a
n
d
th
e
in
tr
icate
n
atu
r
e
o
f
th
e
T
am
il
lan
g
u
ag
e.
T
h
e
in
tr
o
d
u
ct
io
n
o
f
co
d
e
-
m
ix
in
g
,
in
teg
r
atin
g
m
u
ltip
le
lan
g
u
ag
e
s
in
co
m
m
u
n
icatio
n
,
ad
d
s
co
m
p
lex
ity
to
s
en
tim
en
t
a
n
aly
s
is
b
y
in
c
o
r
p
o
r
atin
g
d
iv
er
s
e
lin
g
u
is
tic
elem
en
ts
.
Ad
d
r
ess
in
g
th
ese
o
b
s
tacle
s
d
em
an
d
s
s
p
ec
ialized
ap
p
r
o
ac
h
es
t
h
at
ac
co
u
n
t
f
o
r
th
e
in
tr
icac
ies
o
f
b
o
th
th
e
T
am
il
l
an
g
u
ag
e
an
d
co
d
e
-
m
ix
ed
te
x
t,
u
n
d
e
r
s
co
r
in
g
t
h
e
n
ec
ess
ity
f
o
r
d
ed
icate
d
r
esear
ch
an
d
m
eth
o
d
o
lo
g
ies
in
th
is
d
is
tin
ctiv
e
d
o
m
ain
.
T
h
is
p
a
p
e
r
u
s
es
wo
r
d
em
b
e
d
d
in
g
m
et
h
o
d
s
an
d
m
ac
h
in
e
lear
n
in
g
(
ML
)
t
o
class
if
y
T
a
m
il
ly
r
ical
twee
t
s
tatem
en
ts
.
E
x
p
er
im
en
ts
n
o
tab
ly
em
p
h
asize
Fas
tTe
x
t
wo
r
d
em
b
ed
d
in
g
as
th
e
m
o
s
t
ef
f
ec
ti
v
e
m
eth
o
d
,
s
h
o
wca
s
in
g
s
u
p
er
i
o
r
r
esu
lts
with
a
r
em
ar
k
a
b
le
7
8
%
ac
cu
r
ac
y
wh
e
n
co
u
p
led
with
t
h
e
s
u
p
p
o
r
t v
ec
t
o
r
class
if
icatio
n
(
SVC
)
m
o
d
el.
T
h
e
s
u
b
s
eq
u
en
t
s
ec
tio
n
s
o
f
th
e
p
ap
er
a
r
e
s
tr
u
ctu
r
e
d
as
f
o
llo
w
s
:
s
ec
tio
n
1
d
elin
ea
tes
th
e
n
ec
ess
ity
o
f
s
en
tim
en
t
an
aly
s
is
f
o
r
th
e
T
am
il
lan
g
u
ag
e
an
d
id
e
n
tifie
s
r
esear
ch
g
ap
s
.
Sectio
n
2
p
r
o
v
id
es
an
o
v
er
v
iew
o
f
r
ec
en
t
r
esear
ch
en
d
ea
v
o
r
s
.
Se
ctio
n
3
o
u
tlin
es
th
e
p
r
o
p
o
s
ed
m
eth
o
d
o
l
o
g
y
.
Sectio
n
4
s
h
o
w
ca
s
es
ex
p
er
im
en
tal
r
esu
lts
an
d
en
g
ag
es in
d
is
cu
s
s
io
n
.
T
h
e
p
ap
e
r
co
n
cl
u
d
es in
s
e
ctio
n
5
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
o
f
f
er
s
an
o
u
tlin
e
o
f
cu
r
r
e
n
t
r
esear
ch
ef
f
o
r
ts
in
t
h
e
d
o
m
ain
o
f
T
am
il
s
en
tim
en
t
an
aly
s
is
.
Se
et
a
l.
[
6
]
u
tili
ze
d
ML
alg
o
r
ith
m
s
,
in
clu
d
i
n
g
s
u
p
p
o
r
t
v
e
cto
r
m
ac
h
in
e
(
SVM
)
,
Ma
x
en
t
class
if
ier
,
d
ec
is
io
n
tr
ee
(
DT
)
,
a
n
d
Naiv
e
B
ay
es,
to
class
if
y
T
am
il
m
o
v
ie
r
ev
iews
in
to
p
o
s
itiv
e
an
d
n
eg
ati
v
e
ca
teg
o
r
ies.
T
h
e
d
ataset,
co
llected
f
r
o
m
v
ar
io
u
s
web
s
o
u
r
ce
s
,
in
co
r
p
o
r
ate
d
f
ea
tu
r
es
f
r
o
m
T
a
m
ilS
en
tiwo
r
d
n
et,
with
SVM
d
em
o
n
s
tr
atin
g
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
,
ac
h
ie
v
in
g
a
n
ac
cu
r
ac
y
o
f
7
5
.
9
%.
T
h
av
ar
ee
s
an
an
d
Ma
h
esan
[
7
]
cr
itically
an
aly
ze
r
ec
en
t
liter
atu
r
e
o
n
s
en
tim
e
n
t
an
al
y
s
is
em
p
lo
y
in
g
T
am
il
tex
t,
co
n
clu
d
in
g
t
h
at
SVM
a
n
d
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN
)
class
if
ier
s
u
s
in
g
ter
m
f
r
eq
u
en
cy
-
in
v
er
s
e
d
o
cu
m
e
n
t
f
r
eq
u
en
cy
(
TF
-
I
DF
)
a
n
d
W
o
r
d
2
v
ec
f
ea
tu
r
es
o
u
tp
er
f
o
r
m
g
r
am
m
ar
r
u
le
-
b
ased
class
if
icatio
n
s
an
d
o
th
er
class
if
ier
s
.
Usi
n
g
d
if
f
er
en
t
co
r
p
o
r
a
an
d
f
ea
tu
r
e
r
ep
r
esen
tatio
n
tech
n
iq
u
es,
T
h
av
a
r
ee
s
an
an
d
Ma
h
esan
[
8
]
e
x
p
er
im
e
n
ted
with
v
ar
io
u
s
s
en
tim
en
t
an
aly
s
is
ap
p
r
o
ac
h
e
s
,
in
clu
d
in
g
lex
ico
n
-
b
ased
,
s
u
p
er
v
is
ed
ML
,
h
y
b
r
id
,
a
n
d
clu
s
ter
in
g
with
b
ag
o
f
wo
r
d
m
eth
o
d
s
.
A
m
ax
im
u
m
a
cc
u
r
ac
y
o
f
7
9
%
was
attain
ed
f
o
r
th
e
UJ_
C
o
r
p
u
s
_
Op
in
io
n
s
_
No
u
n
s
co
r
p
u
s
u
s
in
g
Fas
tTe
x
t
in
th
e
s
u
p
er
v
is
ed
ML
ap
p
r
o
ac
h
,
in
co
r
p
o
r
atin
g
b
o
th
b
asic
an
d
tr
ad
itio
n
al
f
ea
tu
r
e
s
.
B
ab
u
an
d
Sri
[
9
]
in
tr
o
d
u
ce
d
h
y
b
r
id
d
ee
p
lear
n
in
g
ap
p
r
o
ac
h
es,
in
clu
d
i
n
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
-
b
id
ir
ec
tio
n
al
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
C
NN
-
B
iLST
M
)
,
C
NN
-
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
L
STM
)
,
a
n
d
C
NN
-
b
id
ir
ec
tio
n
al
g
ated
r
ec
u
r
r
en
t
u
n
it
(
B
iGR
U
)
,
lev
er
ag
in
g
to
o
ls
s
u
p
p
o
r
tin
g
th
e
T
am
il
lan
g
u
a
g
e
f
o
r
d
ata
p
r
ep
a
r
atio
n
.
T
h
e
m
o
d
els
wer
e
ev
alu
ated
b
ased
o
n
m
et
r
ics
lik
e
ac
cu
r
ac
y
,
r
ec
all,
an
d
F1
,
r
ev
ea
lin
g
th
at
C
NN
-
B
iLST
M
ac
h
iev
ed
th
e
h
ig
h
est ac
cu
r
ac
y
(
8
0
.
2
%)
an
d
F1
-
s
co
r
e
(
0
.
6
4
)
co
m
p
a
r
ed
to
o
th
er
m
o
d
els,
ef
f
ec
tiv
ely
class
if
y
in
g
s
en
tim
en
ts
in
T
am
il
m
o
v
ie
r
ev
iews.
Kis
h
o
r
e
et
a
l.
[
1
0
]
em
p
lo
y
e
d
ML
m
o
d
els
f
o
r
s
en
tim
en
t
an
aly
s
is
in
T
am
il
an
d
T
u
lu
lan
g
u
ag
es,
u
tili
zin
g
a
co
d
e
-
m
i
x
ed
d
ataset
f
r
o
m
s
o
cial
m
e
d
ia
.
Ach
iev
in
g
6
4
%
ac
c
u
r
ac
y
a
n
d
a
4
3
%
m
ac
r
o
F1
s
co
r
e
f
o
r
T
am
il
an
d
6
6
%
ac
c
u
r
ac
y
an
d
5
1
%
m
ac
r
o
F1
s
co
r
e
f
o
r
T
u
lu
with
T
F
-
I
DF
f
ea
tu
r
e
ex
tr
ac
tio
n
,
th
eir
s
tu
d
y
h
ig
h
lig
h
ts
th
e
e
f
f
icac
y
o
f
th
e
T
F
-
I
DF
with
lo
g
is
tic
r
eg
r
ess
io
n
(
L
R
)
m
o
d
el,
em
p
h
a
s
izin
g
its
p
o
ten
tial
ap
p
licatio
n
s
in
a
d
d
r
ess
in
g
s
o
c
ial
is
s
u
es
an
d
f
o
s
ter
in
g
in
clu
s
i
v
ity
o
n
lin
e.
T
h
e
r
esear
ch
f
in
d
i
n
g
s
u
n
e
q
u
iv
o
ca
lly
in
d
icate
th
at
co
m
b
i
n
in
g
ML
m
o
d
els with
wo
r
d
em
b
ed
d
in
g
s
p
r
o
d
u
ce
s
b
etter
r
esu
lts
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
s
tr
u
ct
u
r
ed
in
to
two
d
is
tin
ctiv
e
p
h
ase
s
.
I
n
th
e
in
itial p
h
as
e,
wo
r
d
e
m
b
ed
d
in
g
s
ar
e
g
en
er
ated
f
o
r
ea
ch
w
o
r
d
i
n
T
am
il
tex
t
u
s
in
g
f
o
u
r
d
iv
er
s
e
em
b
ed
d
in
g
m
et
h
o
d
s
:
co
u
n
t
,
T
F
-
I
DF,
Hash
in
g
,
W
o
r
d
2
Vec
,
an
d
Fas
tTe
x
t
.
T
h
ese
tech
n
iq
u
es
ar
e
em
p
lo
y
ed
to
v
ec
to
r
ize
th
e
T
am
il
tex
t,
p
r
o
v
id
in
g
a
co
m
p
r
eh
e
n
s
iv
e
r
ep
r
esen
tatio
n
o
f
th
e
s
em
an
tic
an
d
co
n
tex
tu
a
l
in
f
o
r
m
atio
n
p
r
esen
t
in
th
e
la
n
g
u
ag
e
.
Mo
v
in
g
to
th
e
s
ec
o
n
d
p
h
ase,
ei
g
h
t
d
is
tin
c
t
ML
m
eth
o
d
s
ar
e
em
p
lo
y
e
d
.
E
ac
h
m
eth
o
d
is
in
d
iv
id
u
ally
tr
ain
ed
an
d
test
ed
to
ev
alu
ate
its
class
if
icatio
n
p
er
f
o
r
m
an
ce
,
t
h
er
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y
en
ab
lin
g
a
th
o
r
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en
t
o
f
th
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e
f
f
ec
tiv
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o
f
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e
p
r
o
p
o
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ed
ap
p
r
o
ac
h
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as d
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d
in
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u
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o
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d
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ain
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g
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els in
s
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tim
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aly
s
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d
r
elate
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NL
P a
p
p
licatio
n
s
[
1
1
]
.
2
.
2
.
Da
t
a
c
lea
nin
g
Data
clea
n
in
g
in
th
e
co
n
tex
t
o
f
T
am
il
tex
t
(
ly
r
ics)
in
v
o
lv
es
th
e
r
em
o
v
al
o
f
s
p
ec
ia
l
s
y
m
b
o
ls
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alp
h
ab
ets,
an
d
n
u
m
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er
s
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en
s
u
r
e
a
r
ef
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ed
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d
s
tan
d
ar
d
ize
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d
ataset.
T
h
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p
r
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ce
s
s
en
h
an
ce
s
th
e
q
u
ality
o
f
t
h
e
tex
t
d
ata
b
y
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atin
g
u
n
n
e
ce
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s
ar
y
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en
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th
at
m
ay
n
o
t
co
n
tr
ib
u
te
to
t
h
e
s
en
tim
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t
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aly
s
is
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h
e
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n
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g
s
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s
ty
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ically
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u
d
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ip
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m
ar
k
s
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p
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h
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ter
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n
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n
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m
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es,
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in
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a
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r
ep
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ce
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tex
t
m
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co
n
d
u
civ
e
to
ac
cu
r
ate
s
en
tim
en
t
an
aly
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is
.
T
h
is
m
eticu
lo
u
s
clea
n
in
g
aid
s
in
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ea
tin
g
a
s
tr
ea
m
lin
ed
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d
u
n
if
o
r
m
d
ataset,
o
p
tim
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e
s
u
b
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eq
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en
t
an
aly
s
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d
m
o
d
el
tr
ain
in
g
s
tag
es.
2
.
3
.
Vec
t
o
rizing
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r
e
m
bedd
i
ng
Vec
to
r
izin
g
o
r
em
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ed
d
in
g
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il
tex
t
in
v
o
lv
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tr
a
n
s
f
o
r
m
in
g
tex
tu
al
d
ata
i
n
to
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u
m
e
r
ical
r
ep
r
esen
tatio
n
s
th
at
ca
p
tu
r
e
s
em
an
tic
an
d
co
n
tex
t
u
al
in
f
o
r
m
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n
.
Var
io
u
s
tech
n
iq
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es,
s
u
ch
as
co
u
n
t
v
ec
to
r
izatio
n
,
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F
-
I
DF
,
Hash
in
g
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o
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in
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t
o
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n
a
h
ig
h
-
d
im
en
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io
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al
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p
ac
e
[
1
2
]
,
[
1
3
]
.
T
h
ese
v
ec
to
r
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n
m
eth
o
d
s
ar
e
c
r
u
cial
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p
r
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els,
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aly
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is
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y
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wo
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d
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d
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s
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ese
tech
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o
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ith
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ip
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2
.
3
.
1
.
Co
un
t
v
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t
o
rize
r
C
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n
t
v
ec
to
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izatio
n
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a
s
im
p
le
tech
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iq
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e
f
o
r
tr
an
s
f
o
r
m
in
g
tex
t
d
ata
in
to
n
u
m
er
ical
f
o
r
m
at
[
1
4
]
.
I
t
r
ep
r
esen
ts
ea
ch
d
o
cu
m
e
n
t
as
a
v
ec
to
r
o
f
ter
m
f
r
e
q
u
en
cies.
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r
T
am
il
s
en
tim
en
t
class
if
ic
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,
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ch
u
n
iq
u
e
wo
r
d
in
th
e
c
o
r
p
u
s
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ig
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in
d
ex
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th
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co
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n
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s
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to
c
o
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s
tr
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ct
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v
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to
r
r
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p
r
esen
tatio
n
.
(
,
)
=
2
.
3
.
2
.
TF
-
I
DF
v
ec
t
o
rize
r
T
h
e
T
F
-
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DF
ap
p
r
o
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h
ass
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a
wo
r
d
'
s
s
ig
n
if
ican
ce
in
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t
ab
o
u
t
a
g
r
o
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p
o
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d
o
cu
m
e
n
ts
[
1
5
]
.
I
t a
p
p
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h
ts
to
wo
r
d
s
u
s
in
g
T
F a
n
d
I
DF.
(
,
,
)
=
(
,
)
(
,
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
2
2
5
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8
7
7
6
I
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&
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4
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3
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Dec
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20
2
5
:
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4
9
944
W
h
er
e:
TF
(
w
,
d
)
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N
umb
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r
en
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of
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d
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.
3
.
3
.
H
a
s
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v
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t
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rize
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tex
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at
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a
h
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ap
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co
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v
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tin
g
th
em
in
to
f
i
x
ed
-
s
ize
v
e
cto
r
s
[
1
5
]
.
2
.
3
.
4
.
Wo
rd2
Vec
(
co
ntinuo
us
ba
g
o
f
wo
rds
)
W
o
r
d
2
v
ec
is
a
p
o
p
u
lar
w
o
r
d
em
b
ed
d
in
g
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h
n
iq
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e
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s
ed
in
NL
P
task
s
,
s
u
ch
as
s
en
tim
en
t
an
aly
s
is
[
1
6
]
.
I
n
th
e
co
n
tex
t
o
f
T
a
m
il
s
en
tim
en
t
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if
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n
,
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o
r
d
2
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em
p
lo
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th
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co
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tin
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o
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C
B
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th
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d
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an
tics
o
f
wo
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d
s
.
2
.
3
.
5
.
F
a
s
t
T
ex
t
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tTe
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t
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an
ex
ten
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io
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o
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r
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th
at
c
o
n
s
id
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s
s
u
b
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wo
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i
n
f
o
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m
atio
n
.
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t
b
r
ea
k
s
wo
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d
s
in
to
s
m
aller
n
-
g
r
am
s
u
b
-
wo
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s
an
d
r
ep
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em
a
s
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m
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f
t
h
ese
s
u
b
-
wo
r
d
em
b
e
d
d
in
g
s
[
1
7
]
.
T
h
is
p
ar
ticu
lar
ly
b
e
n
ef
its
lan
g
u
a
g
es lik
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am
il,
wh
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e
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d
s
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n
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av
e
co
m
p
lex
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o
r
p
h
o
lo
g
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s
tr
u
ctu
r
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2
.
4
.
Cro
s
s
v
a
lid
a
t
i
o
n a
nd
perf
o
rma
nce
ev
a
lua
t
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Fo
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th
e
v
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o
r
em
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ed
d
in
g
p
r
o
ce
s
s
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a
co
m
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r
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en
s
iv
e
1
0
-
f
o
l
d
cr
o
s
s
-
v
alid
atio
n
[
1
8
]
is
co
n
d
u
cte
d
u
s
in
g
eig
h
t
d
iv
er
s
e
ML
s
ch
em
es,
s
u
ch
a
s
SV
M
,
LR
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B
ay
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r
an
d
o
m
f
o
r
est
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R
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DT
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g
r
ad
ien
t
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o
o
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tin
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(
GB
)
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ex
t
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e
g
r
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d
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t
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o
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tin
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(
XGB)
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an
d
Ad
aBo
o
s
t
[
1
9
]
-
[
2
3
]
.
T
h
is
m
eticu
lo
u
s
ev
alu
atio
n
ass
ess
e
s
th
e
p
er
f
o
r
m
an
ce
o
f
ea
ch
m
o
d
el
in
th
e
co
n
tex
t
o
f
T
am
il
s
en
tim
en
t
class
if
icatio
n
.
T
h
e
p
er
f
o
r
m
an
ce
is
m
ea
s
u
r
ed
ac
r
o
s
s
v
ar
io
u
s
ev
alu
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m
et
r
ics,
in
clu
d
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g
ac
cu
r
ac
y
,
p
r
ec
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io
n
,
r
ec
all
,
an
d
F1
s
co
r
e.
E
ac
h
ML
m
o
d
el
u
n
d
e
r
g
o
es
r
ig
o
r
o
u
s
test
in
g
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d
tr
ain
in
g
ac
r
o
s
s
th
e
1
0
f
o
ld
s
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en
s
u
r
in
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a
r
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b
u
s
t
ass
es
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en
t
o
f
its
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f
icac
y
in
h
an
d
lin
g
th
e
c
o
m
p
lex
ities
o
f
T
am
il
s
en
tim
en
t
an
aly
s
is
.
T
h
e
ch
o
s
en
e
v
alu
atio
n
m
etr
ics p
r
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id
e
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h
o
lis
tic
v
iew
o
f
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
,
ac
co
u
n
tin
g
f
o
r
asp
ec
ts
s
u
ch
as o
v
er
all
co
r
r
ec
tn
ess
(
a
ccu
r
ac
y
)
,
ca
p
ab
ilit
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to
ap
p
r
o
p
r
iately
id
en
tify
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o
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itiv
e
in
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tan
ce
s
(
p
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io
n
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,
ca
p
a
b
ilit
y
to
s
eizu
r
e
all
p
o
s
itiv
e
in
s
tan
ce
s
(
r
ec
all)
,
an
d
t
h
e
h
a
r
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
a
n
d
r
ec
a
ll
(
F1
s
co
r
e
)
[
2
4
]
-
[
3
0
]
.
T
h
is
m
u
ltifa
ce
ted
ev
alu
atio
n
s
tr
ateg
y
h
elp
s
id
en
tify
th
e
ap
p
r
o
p
r
iate
ML
m
o
d
el
f
o
r
ac
h
iev
in
g
o
p
tim
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s
en
tim
en
t
class
if
icatio
n
r
esu
lts
in
th
e
T
am
il c
o
n
tex
t.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ex
p
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im
e
n
tal
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esu
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f
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eth
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d
wer
e
co
n
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u
cted
u
s
in
g
Py
th
o
n
a
n
d
J
u
p
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ter
No
teb
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I
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h
n
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I
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N:
2252
-
8
7
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I
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f
&
C
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m
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T
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n
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d
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ta
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c
a
n
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tac
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stin
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ra
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e
k
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h
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tac
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h
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m
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:
v
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ra
m
k
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lran
g
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@g
m
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.
c
o
m
.
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