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15
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20
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[
1
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[
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Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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,
Vo
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15
,
No
.
3
,
Sep
tem
b
er
20
26
:
1
1
1
5
-
1
1
2
2
1116
T
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p
h
is
ticated
to
o
ls
ac
ce
s
s
ib
le
to
a
b
r
o
ad
e
r
au
d
ien
ce
.
T
h
r
o
u
g
h
th
is
wo
r
k
,
we
aim
to
r
e
d
ef
in
e
th
e
lan
d
s
ca
p
e
o
f
in
v
estme
n
t
to
o
ls
,
p
r
o
v
id
in
g
in
v
esto
r
s
with
a
d
ee
p
er
u
n
d
er
s
tan
d
in
g
o
f
m
ar
k
et
d
y
n
am
ics
an
d
th
e
co
n
f
id
e
n
ce
to
m
a
k
e
in
f
o
r
m
ed
d
ec
is
io
n
s
[
3
]
,
[
4
]
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
e
p
r
e
d
ictio
n
o
f
s
to
ck
p
r
ice
s
is
a
p
r
o
m
in
en
t
ar
ea
o
f
r
esear
ch
in
f
i
n
an
ce
an
d
ar
tific
ial
i
n
tellig
en
ce
d
u
e
to
its
p
o
ten
tial
f
o
r
s
ig
n
i
f
ican
t
f
in
an
cial
r
etu
r
n
s
a
n
d
it
s
in
f
lu
en
ce
o
n
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
ce
s
s
es.
T
h
is
r
ev
iew
h
ig
h
lig
h
ts
th
e
in
teg
r
at
io
n
o
f
m
ac
h
in
e
l
ea
r
n
in
g
m
o
d
els,
s
en
tim
en
t
an
aly
s
is
,
an
d
h
y
b
r
id
ap
p
r
o
ac
h
es,
wh
ich
ar
e
p
iv
o
tal
in
c
r
ea
tin
g
r
o
b
u
s
t
s
to
ck
p
r
e
d
ictio
n
s
y
s
tem
s
.
T
r
ad
itio
n
al
m
ac
h
in
e
lea
r
n
in
g
m
o
d
els
lik
e
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
es
(
SVM)
,
r
an
d
o
m
f
o
r
ests
,
an
d
ar
tifi
cial
n
eu
r
al
n
etwo
r
k
s
(
ANN)
h
av
e
b
ee
n
ex
ten
s
iv
ely
ap
p
lied
to
f
o
r
ec
ast
s
to
ck
p
r
ices.
Ho
wev
er
,
th
ese
m
o
d
el
s
o
f
ten
f
ac
e
lim
itatio
n
s
in
ca
p
tu
r
in
g
tem
p
o
r
al
d
ep
en
d
e
n
cies
p
r
esen
t
in
f
in
a
n
cial
d
ata.
T
h
e
in
tr
o
d
u
ctio
n
o
f
r
ec
u
r
r
e
n
t
n
e
u
r
al
n
etwo
r
k
s
(
R
NNs)
an
d
th
eir
ad
v
an
ce
d
v
ar
ian
t,
L
STM
,
h
as
ad
d
r
ess
ed
t
h
is
ch
allen
g
e
b
y
ef
f
ec
tiv
ely
m
o
d
elin
g
s
eq
u
en
tial
d
ata
[
5
]
.
Ho
ch
r
eiter
an
d
Sch
m
id
h
u
b
e
r
’
s
f
o
u
n
d
atio
n
al
wo
r
k
o
n
L
ST
Ms
d
em
o
n
s
tr
ated
th
eir
ab
ilit
y
to
o
v
er
co
m
e
th
e
v
an
is
h
in
g
g
r
ad
ien
t
p
r
o
b
lem
,
m
ak
in
g
th
em
well
-
s
u
ited
f
o
r
tim
e
-
s
er
ies d
ata
an
aly
s
is
[
2
]
,
[
6
]
.
R
ec
en
t
s
tu
d
ies
h
av
e
u
n
d
er
s
co
r
ed
th
e
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
o
f
L
STM
m
o
d
els
o
v
er
tr
ad
itio
n
al
m
ac
h
in
e
lear
n
in
g
alg
o
r
ith
m
s
.
Z
ah
ee
r
et
a
l.
[
4
]
d
em
o
n
s
tr
at
ed
th
at
L
STM
m
o
d
els,
in
co
r
p
o
r
atin
g
p
ar
am
eter
s
s
u
ch
as
h
is
to
r
ical
p
r
ices
an
d
m
ar
k
et
in
d
ices
,
ex
ce
l
at
ca
p
tu
r
in
g
m
a
r
k
e
t
tr
en
d
s
with
h
ig
h
p
r
ec
is
io
n
[
4
]
.
Similar
ly
,
Kh
an
et
a
l.
[
3
]
co
m
p
ar
ed
L
STM
with
o
th
er
m
ac
h
in
e
lear
n
in
g
tech
n
iq
u
e
s
,
em
p
h
asizin
g
its
ca
p
ab
ilit
y
to
h
an
d
le
n
o
n
-
lin
e
ar
f
in
an
cial
p
atter
n
s
with
g
r
ea
ter
ac
cu
r
ac
y
an
d
s
tab
ilit
y
[
3
]
.
T
h
ese
f
in
d
in
g
s
estab
lis
h
L
STM
as
a
r
o
b
u
s
t
to
o
l
f
o
r
s
to
ck
p
r
ice
f
o
r
ec
asti
n
g
,
p
ar
ticu
lar
ly
in
a
d
d
r
ess
in
g
th
e
s
eq
u
en
tial
n
atu
r
e
o
f
m
ar
k
et
d
ata.
Sen
tim
en
t
an
aly
s
is
f
u
r
th
er
e
n
h
an
ce
s
s
to
ck
p
r
ed
ictio
n
b
y
lev
er
ag
in
g
n
atu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
(
NL
P)
to
e
x
tr
ac
t
m
ar
k
et
s
en
tim
en
t
f
r
o
m
u
n
s
tr
u
ctu
r
ed
t
ex
tu
al
d
ata
[
7
]
,
in
clu
d
i
n
g
n
ews
ar
ticles,
f
in
an
cial
r
ep
o
r
ts
,
an
d
s
o
cial
m
ed
ia
[
8
]
.
T
etlo
ck
’
s
s
em
in
al
s
tu
d
y
(
2
0
0
7
)
h
ig
h
lig
h
ted
th
e
s
ig
n
if
ican
t
im
p
ac
t
o
f
m
ed
ia
s
en
tim
en
t
o
n
s
to
ck
p
r
ice
m
o
v
em
en
ts
,
la
y
in
g
th
e
f
o
u
n
d
a
tio
n
f
o
r
s
en
tim
e
n
t
-
d
r
iv
e
n
m
ar
k
et
an
al
y
s
is
[
9
]
.
Ad
v
an
ce
d
NL
P
m
o
d
els
lik
e
Dis
til
R
o
B
E
R
T
a
h
av
e
s
ig
n
if
ic
an
tly
im
p
r
o
v
ed
s
en
tim
en
t
d
et
ec
tio
n
b
y
o
f
f
er
i
n
g
f
aster
an
d
m
o
r
e
p
r
ec
is
e
ca
teg
o
r
izatio
n
.
Sh
a
h
et
a
l.
[
1
0
]
n
o
ted
th
e
ef
f
ec
tiv
e
n
ess
o
f
co
m
b
in
in
g
s
en
tim
en
t
an
aly
s
is
with
m
ac
h
in
e
lear
n
in
g
m
o
d
els f
o
r
h
y
b
r
i
d
f
i
n
an
cial
f
o
r
ec
asti
n
g
s
y
s
tem
s
,
wh
ich
in
co
r
p
o
r
ate
q
u
alitativ
e
d
ata
to
e
n
h
an
ce
p
r
ed
ictio
n
ac
cu
r
ac
y
[
1
0
]
-
[
1
2
]
.
Ad
d
itio
n
all
y
,
th
e
R
ef
in
itiv
Ma
r
k
etPs
y
ch
f
r
am
ewo
r
k
(
2
0
2
1
)
q
u
an
tifie
d
s
u
s
tain
ab
ilit
y
s
en
ti
m
en
t
u
s
in
g
g
lo
b
al
n
ews
an
d
s
o
cial
m
ed
ia
d
ata,
s
h
o
wca
s
in
g
th
e
r
o
le
o
f
s
en
tim
en
t
m
etr
ics in
in
ter
p
r
etin
g
in
v
esto
r
b
eh
av
i
o
r
an
d
m
ar
k
et
d
y
n
am
i
cs [
1
]
.
Hy
b
r
id
a
p
p
r
o
ac
h
es
th
at
c
o
m
b
i
n
e
q
u
a
n
titativ
e
f
in
a
n
cial
d
ata
with
q
u
alitativ
e
s
en
tim
en
t
a
n
a
ly
s
is
h
av
e
p
r
o
v
e
n
to
b
e
h
ig
h
ly
ef
f
ec
tiv
e.
Fo
r
e
x
am
p
le,
Z
ah
ee
r
et
a
l.
[
4
]
d
em
o
n
s
tr
ated
th
e
b
en
e
f
its
o
f
in
teg
r
atin
g
s
en
tim
en
t
s
co
r
es
with
L
STM
-
b
ased
p
r
ed
ictio
n
s
,
r
ep
o
r
tin
g
s
ig
n
if
ican
t
im
p
r
o
v
em
e
n
ts
in
b
o
th
p
r
ec
is
io
n
an
d
r
eliab
ilit
y
[
4
]
,
[1
3
]
.
Allen
et
a
l.
[
1
4
]
ex
p
l
o
r
ed
th
e
i
n
teg
r
atio
n
o
f
m
ac
h
in
e
-
r
ea
d
ab
le
n
e
ws
in
to
tr
ad
itio
n
al
m
o
d
els
f
o
r
v
o
latilit
y
p
r
ed
icti
o
n
,
s
h
o
win
g
th
at
s
en
tim
en
t
-
e
n
r
ich
ed
m
o
d
els
o
u
tp
er
f
o
r
m
p
u
r
ely
q
u
an
titativ
e
o
n
es,
p
ar
ticu
lar
ly
d
u
r
in
g
p
er
i
o
d
s
o
f
h
eig
h
te
n
ed
m
ar
k
et
u
n
c
er
tain
ty
[
1
4
]
,
[
1
5
]
.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
ch
allen
g
es
s
u
ch
as
d
ata
q
u
ality
,
in
ter
p
r
etab
ilit
y
,
an
d
th
e
d
y
n
am
ic
n
atu
r
e
o
f
f
in
a
n
cia
l
m
ar
k
ets
r
em
ain
.
E
m
p
h
asized
th
e
n
ee
d
f
o
r
m
o
r
e
s
o
p
h
is
ticated
h
y
b
r
id
f
r
am
e
wo
r
k
s
th
at
i
n
co
r
p
o
r
ate
r
ea
l
-
tim
e
d
ata
s
tr
ea
m
s
a
n
d
m
ac
r
o
ec
o
n
o
m
ic
in
d
icat
o
r
s
f
o
r
g
r
ea
ter
ad
a
p
tab
ilit
y
[
1
0
]
,
[
1
6
].
3.
M
E
T
H
O
D
T
h
e
p
r
o
p
o
s
ed
s
y
s
tem
co
m
b
in
es
s
en
tim
en
t
an
aly
s
is
an
d
tim
e
-
s
er
ies
f
o
r
ec
asti
n
g
to
p
r
ed
ict
s
to
ck
p
r
ice
tr
en
d
s
ef
f
ec
tiv
ely
,
as
o
u
tlin
ed
in
Fig
u
r
e
1
.
T
h
e
wo
r
k
f
lo
w
b
e
g
in
s
wh
en
th
e
u
s
er
p
r
o
m
p
ts
th
e
s
y
s
tem
f
o
r
s
to
ck
p
r
ice
p
r
ed
ictio
n
s
.
T
h
e
Op
en
AI
L
L
M
is
u
s
ed
to
m
an
ag
e
d
ata
f
lo
w
an
d
in
ter
f
ac
e
in
ter
ac
tio
n
s
,
ca
llin
g
ap
p
r
o
p
r
iate
to
o
ls
to
g
at
h
er
th
e
r
eq
u
ir
ed
in
p
u
ts
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
I
n
s
ig
h
t in
ve
s
t:
s
en
timen
t
-
a
w
a
r
e
s
to
ck
p
r
ed
ictio
n
u
s
in
g
LS
TM
a
n
d
c
o
n
ve
r
s
a
tio
n
a
l in
terf
a
ce
(
A
n
kit
P
a
n
d
e
)
1117
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o
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F
l
as
k
s
e
r
v
e
r
,
w
h
e
r
e
s
e
n
t
i
m
e
n
t
a
n
a
l
y
s
is
is
p
e
r
f
o
r
m
e
d
t
o
d
e
r
i
v
e
s
e
n
t
i
m
e
n
t
s
c
o
r
es
,
w
h
i
c
h
a
r
e
s
u
b
s
e
q
u
e
n
t
l
y
p
a
s
s
e
d
i
n
t
o
t
h
e
p
r
e
d
i
c
ti
o
n
f
r
a
m
e
w
o
r
k
.
T
h
e
p
r
o
c
e
s
s
e
d
d
at
a
is
f
e
d
i
n
t
o
a
b
i
-
d
i
r
e
c
t
i
o
n
a
l
LS
TM
[
2
]
m
o
d
e
l
t
h
a
t
p
r
e
d
i
ct
s
t
h
e
s
t
o
c
k
'
s
f
u
t
u
r
e
cl
o
s
i
n
g
p
r
i
c
e
f
o
r
t
h
e
n
e
x
t
d
a
y
[1
7
]
,
[
18
]
.
T
h
e
m
o
d
e
l
i
n
t
e
g
r
at
e
s
h
i
s
t
o
r
i
c
a
l
p
r
i
c
e
t
r
e
n
d
s
a
n
d
s
e
n
t
i
m
e
n
t
a
n
a
l
y
s
is
o
u
t
p
u
ts
t
o
d
el
i
v
e
r
p
r
ec
i
s
e
p
r
e
d
i
c
t
i
o
n
s
.
T
h
e
p
r
e
d
ic
t
i
o
n
r
e
s
u
l
ts
,
al
o
n
g
w
i
t
h
s
e
n
ti
m
e
n
t
i
n
t
e
r
p
r
e
t
at
i
o
n
s
,
a
r
e
t
h
e
n
r
o
u
t
e
d
b
a
c
k
t
o
t
h
e
O
p
e
n
A
I
L
L
M
,
w
h
i
c
h
d
is
p
l
a
y
s
t
h
e
m
t
o
t
h
e
u
s
e
r
i
n
a
s
i
m
p
l
i
f
i
e
d
,
u
s
e
r
-
f
r
i
e
n
d
l
y
m
a
n
n
e
r
.
T
h
i
s
a
p
p
r
o
a
c
h
i
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te
g
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a
t
es
a
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o
m
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t
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o
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d
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a
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e
d
m
a
c
h
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e
l
ea
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n
i
n
g
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e
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h
n
i
q
u
e
s
,
a
n
d
a
c
o
n
v
e
r
s
a
t
i
o
n
al
i
n
t
e
r
f
ac
e
,
p
r
o
v
i
d
i
n
g
a
n
i
n
t
u
i
t
i
v
e
a
n
d
r
e
l
iab
l
e
t
o
o
l
f
o
r
s
t
o
c
k
m
a
r
k
e
t
f
o
r
e
ca
s
t
i
n
g
[
3
]
,
[
19
]
,
[
20
].
Fig
u
r
e
1
.
W
o
r
k
f
lo
w
o
f
s
to
ck
p
r
ice
p
r
ed
ictio
n
with
s
en
tim
en
t
an
aly
s
is
3
.
1
.
M
o
del t
ra
ini
ng
3
.
1
.
1
.
Da
t
a
s
et
c
o
llect
io
n
His
to
r
ical
s
to
ck
p
r
ice
d
ata
f
o
r
1
9
s
to
ck
s
lis
ted
o
n
th
e
Natio
n
al
Sto
ck
E
x
ch
a
n
g
e
(
NSE)
w
as
o
b
tain
ed
f
o
r
a
f
iv
e
-
y
ea
r
p
er
io
d
,
s
p
an
n
i
n
g
f
r
o
m
Sep
tem
b
er
1
5
,
2
0
1
9
,
to
Octo
b
er
3
1
,
2
0
2
4
.
T
h
e
d
a
taset
in
clu
d
es
k
ey
f
in
an
cial
attr
ib
u
tes
s
u
ch
as
D
ate,
Op
en
,
Hig
h
,
L
o
w,
C
lo
s
e,
Vo
lu
m
e,
C
h
an
g
e%,
an
d
Na
m
e.
Fin
an
cial
n
ews
s
n
ip
p
ets
wer
e
r
etr
iev
ed
u
s
in
g
a
New
s
API
,
en
s
u
r
in
g
r
ea
l
-
ti
m
e
s
en
tim
en
t
ev
alu
atio
n
.
A
Sto
ck
Pric
e
API
was
u
s
ed
to
ex
tr
ac
t th
e
h
is
to
r
ical
p
r
ice
tr
en
d
s
.
T
h
e
h
is
to
r
ical
d
ata
was so
u
r
ce
d
f
r
o
m
th
e
Yah
o
o
Fin
an
ce
web
s
ite.
3
.
1
.
2
.
Da
t
a
prepro
ce
s
s
ing
B
ef
o
r
e
tr
ain
in
g
,
t
h
e
d
ataset
u
n
d
er
wen
t
p
r
ep
r
o
ce
s
s
in
g
to
en
s
u
r
e
it
was
clea
n
,
u
n
if
o
r
m
,
a
n
d
s
u
itab
le
f
o
r
th
e
m
o
d
el'
s
r
eq
u
ir
em
en
ts
.
Miss
in
g
v
alu
es
in
th
e
"Vo
lu
m
e"
co
lu
m
n
wer
e
r
ep
lace
d
with
0
to
m
ain
tain
d
ata
u
n
if
o
r
m
ity
.
C
ateg
o
r
ical
s
to
ck
n
am
es
in
th
e
"Na
m
e"
co
lu
m
n
wer
e
tr
an
s
f
o
r
m
ed
u
s
in
g
o
n
e
-
h
o
t
en
co
d
in
g
(
1
)
,
allo
win
g
th
e
m
o
d
el
to
p
r
o
ce
s
s
th
em
as n
u
m
er
ical
f
e
atu
r
es.
(
)
=
=
[
1
,
2
,
.
.
.
,
]
ℎ
=
{
1
,
=
0
,
ℎ
(
1
)
Fin
ally
,
f
ea
tu
r
e
s
elec
tio
n
was
p
er
f
o
r
m
ed
to
r
etai
n
o
n
ly
t
h
e
r
elev
an
t
co
lu
m
n
s
,
in
clu
d
in
g
Op
en
,
Hig
h
,
L
o
w,
Vo
lu
m
e,
C
lo
s
e,
an
d
th
e
en
co
d
ed
s
to
ck
n
am
es,
wh
ic
h
wer
e
e
s
s
en
tial f
o
r
tr
ain
in
g
.
3
.
2
.
M
o
del a
rc
hite
ct
ure
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
u
tili
ze
s
a
s
eq
u
en
ce
o
f
lay
er
s
to
ca
p
tu
r
e
tem
p
o
r
al
d
e
p
en
d
e
n
cies
an
d
ex
tr
ac
t
m
ea
n
in
g
f
u
l
f
ea
tu
r
es
f
o
r
ac
cu
r
ate
s
to
ck
p
r
ice
p
r
ed
ictio
n
.
T
h
e
ar
ch
itectu
r
e
d
esig
n
is
v
is
u
ali
ze
d
in
Fig
u
r
e
2
an
d
is
d
esig
n
ed
as f
o
llo
ws:
3
.
2
.
1
.
B
idi
re
ct
io
na
l LS
T
M
la
y
er
s
T
h
e
m
o
d
el
i
n
co
r
p
o
r
ates
two
B
id
ir
ec
tio
n
al
L
STM
lay
er
s
,
ea
ch
co
n
s
is
tin
g
o
f
1
0
0
u
n
its
.
L
ST
M
is
a
ty
p
e
o
f
R
NN
th
at
m
itig
ates
t
h
e
v
a
n
is
h
in
g
g
r
ad
ie
n
t
p
r
o
b
le
m
b
y
in
tr
o
d
u
ci
n
g
g
ates
-
in
p
u
t,
f
o
r
g
et,
a
n
d
o
u
t
p
u
t
g
ates
-
th
at
r
e
g
u
late
th
e
f
lo
w
o
f
in
f
o
r
m
atio
n
.
T
h
ese
la
y
er
s
ar
e
d
esig
n
ed
to
ca
p
tu
r
e
b
o
th
p
ast
a
n
d
f
u
tu
r
e
d
ep
en
d
e
n
cies
with
in
s
eq
u
e
n
tial
d
ata
b
y
p
r
o
ce
s
s
in
g
in
p
u
ts
in
f
o
r
war
d
an
d
b
ac
k
war
d
d
ir
ec
tio
n
s
[
21
]
.
T
h
e
d
etailed
m
ath
em
atica
l
o
p
er
atio
n
s
an
d
g
atin
g
m
ec
h
an
is
m
s
o
f
L
STM
n
etwo
r
k
s
ar
e
well
-
d
o
cu
m
en
te
d
in
th
e
liter
atu
r
e
[
1
7
]
,
[
2
2
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
7
7
6
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
,
Vo
l.
15
,
No
.
3
,
Sep
tem
b
er
20
26
:
1
1
1
5
-
1
1
2
2
1118
Fig
u
r
e
2.
Ar
c
h
itectu
r
e
o
f
th
e
L
STM
m
o
d
el
3
.
2
.
2
.
Dro
po
ut
la
y
er
s
T
o
p
r
ev
en
t
o
v
er
f
itti
n
g
,
d
r
o
p
o
u
t
lay
er
s
ar
e
ap
p
lied
af
ter
ea
ch
B
id
ir
ec
tio
n
al
L
STM
lay
er
with
a
r
ate
o
f
2
0
%.
T
h
is
r
an
d
o
m
ly
s
ets 2
0
%
o
f
th
e
n
e
u
r
o
n
s
to
ze
r
o
d
u
r
i
n
g
t
r
ain
in
g
,
e
n
s
u
r
in
g
r
o
b
u
s
t f
ea
tu
r
e
ex
tr
ac
tio
n
.
3
.
2
.
3
.
Dense
la
y
er
s
A
Den
s
e
lay
er
with
5
0
u
n
its
an
d
R
eL
U
ac
tiv
atio
n
r
ef
in
es
th
e
ex
tr
ac
ted
f
ea
tu
r
es.
T
h
e
R
eL
U
ac
tiv
atio
n
f
u
n
cti
o
n
in
tr
o
d
u
ce
s
n
o
n
-
lin
ea
r
ity
:
(
)
=
(
0
,
)
(
2
)
3
.
3
.
E
mo
t
io
na
l quo
t
ient
Fin
an
cial
n
ews
ar
ticles
ar
e
g
ath
er
ed
u
s
in
g
API
s
an
d
w
eb
s
cr
ap
er
s
,
ex
tr
ac
tin
g
d
etai
ls
s
u
ch
as
h
ea
d
lin
es,
co
n
ten
t,
an
d
p
u
b
lic
atio
n
s
o
u
r
ce
s
to
p
r
o
v
id
e
r
elev
an
t
in
p
u
ts
f
o
r
s
en
tim
en
t
an
aly
s
is
.
E
ac
h
ar
ticle
is
an
aly
ze
d
with
NL
P
-
b
ased
s
en
tim
en
t
s
co
r
in
g
,
ass
ig
n
in
g
b
o
th
a
s
en
tim
en
t
lab
el
(
p
o
s
itiv
e,
n
e
u
tr
al,
o
r
n
eg
ativ
e)
an
d
a
n
u
m
er
ical
s
en
tim
en
t
s
co
r
e
(
0
–
1
)
to
in
d
icate
co
n
f
id
en
ce
[
23
]
.
Ar
ticles
ar
e
f
u
r
th
er
c
ateg
o
r
ized
in
to
k
e
y
ty
p
es,
s
u
ch
as
ea
r
n
in
g
s
r
ep
o
r
ts
o
r
g
eo
p
o
liti
ca
l
ev
en
ts
,
to
im
p
r
o
v
e
c
o
n
tex
tu
al
u
n
d
er
s
tan
d
in
g
an
d
p
r
io
r
itize
im
p
ac
tf
u
l
n
ews
[
1
]
,
[
1
0
]
.
W
e
ig
h
ted
s
en
tim
en
t
s
co
r
e
s
(
W
S
S)
ar
e
co
m
p
u
ted
u
s
in
g
weig
h
ts
b
ased
o
n
s
o
u
r
ce
cr
ed
ib
ilit
y
an
d
ca
teg
o
r
y
r
elev
an
ce
.
T
h
ese
s
co
r
es
ar
e
ag
g
r
e
g
ated
d
aily
in
to
a
d
aily
s
en
ti
m
en
t
s
co
r
e
(
DSS),
wh
ich
r
ef
lects o
v
er
all
m
a
r
k
et
s
en
tim
en
t.
=
×
ℎ
×
ℎ
(
3
)
T
h
e
W
SS
f
o
r
all
ar
ticles o
n
a
g
iv
en
d
a
y
ar
e
a
g
g
r
e
g
ated
to
ca
lcu
late
th
e
(
DSS):
=
∑
=
1
∑
ℎ
=
1
(
4
)
T
h
e
DSS is
s
ca
led
in
to
ac
tio
n
ab
le
ca
teg
o
r
ies:
−
Po
s
itiv
e
(
>
1
.
0
)
−
Neu
tr
al
(
0
.
75
≤
≤
1
.
0
)
−
Neg
ativ
e
(
<
0
.
75
)
[
3
]
,
[
9
]
,
[
2
4
].
Fig
u
r
e
3
clea
r
ly
o
u
tlin
es th
e
p
r
o
ce
s
s
to
ca
lcu
late
DSS
(
EQ
)
3
.
4
.
E
x
perim
ent
a
l pro
ce
du
r
e
−
Data
r
etr
iev
al:
s
to
ck
p
r
ice
d
ata
an
d
f
in
a
n
cial
n
ews
s
n
ip
p
ets we
r
e
co
llected
.
−
Pre
p
r
o
ce
s
s
in
g
:
cl
ea
n
in
g
,
en
co
d
in
g
,
a
n
d
f
ea
tu
r
e
s
elec
tio
n
we
r
e
p
er
f
o
r
m
ed
.
−
Sen
tim
en
t
an
aly
s
is
: n
ews
ar
ticles we
r
e
an
aly
s
ed
to
co
m
p
u
te
s
en
tim
en
t sco
r
es.
−
Mo
d
el
tr
ain
in
g
: t
h
e
L
STM
m
o
d
el
was tr
ain
ed
u
s
in
g
h
is
to
r
ical
s
to
ck
p
r
ices a
n
d
s
en
t
im
en
t sc
o
r
es a
s
in
p
u
t
f
o
r
5
0
ep
o
c
h
s
.
−
Pre
d
ictio
n
g
en
e
r
atio
n
: th
e
tr
ai
n
ed
m
o
d
el
was u
s
ed
to
p
r
ed
ict
th
e
s
to
ck
'
s
n
ex
t
-
d
ay
clo
s
in
g
p
r
ice.
−
E
v
alu
atio
n
:
m
o
d
el
p
er
f
o
r
m
a
n
c
e
was a
s
s
e
s
s
ed
u
s
in
g
m
etr
ics s
u
ch
as m
ea
n
s
q
u
ar
ed
e
r
r
o
r
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