def analysis(Name, mydata, Familyflag, Suspect, Familynames, shift):
tagger = ner.SocketNER(host="localhost", port=8080)
sentences = tokenize.sent_tokenize(mydata)
names = initialze()
pronoun = initialze()
Lname = lastname(Name)
# Tagging out the sentences with the non criminal activities.
for i in sentences:
# shape = '(?:^|(?<= ))(%s)(?:(?= )|$)'
if (
re.findall(".*?(%s).*?" % "|".join(ihatecrime), i, re.IGNORECASE) == []
and re.findall(".*?(%s).*?" % action, i) == []
and senty(i)[1] > 0
and re.findall("%s" % ("|".join(Lname)), i, re.IGNORECASE) != []
):
try:
pronoun = tagger.get_entities(i)[shift]
for pnoun in pronoun:
if (
i == sentences[0]
and (
re.findall("[By|by] (%s)" % pnoun, i) != [] or re.findall("CONTACT: (%s)" % pnoun, i) != []
)
) or re.findall("(%s).*?(%s)" % (pnoun, options), i) != []:
Name = list(set(Name) - set([pnoun]))
elif pnoun in Name or pnoun.title() in Lname:
names.append(pnoun.title())
elif pnoun in Name or pnoun in Lname:
names.append(pnoun)
elif len(pnoun.split()) == 1 and Familyflag == "True" and pnoun in " ".join(Familynames).split():
names.append(familydict[pnoun])
except:
pass
Lastname = list(set(names + Suspect))
Fullname = fulname(Name + Suspect, Lastname)
Fullname.sort(key=len, reverse=True)
a = []
d = []
for i in Fullname:
for j in i.split():
if j not in d and len(i.split()) > 1:
d.append(j)
a.append(i)
Fullname = list(set(a))
Famname = initialze()
# Identifying the family member involved in the crime
if Familyflag == "True":
for i in Fullname:
if i.split()[-1] in Lastname and i not in Fullname:
forward = mydata.split(i)[1].split(". ")[0]
backward = mydata.split(i)[0].split(". ")[-1]
if senty(forward)[1] != 0 or senty(backward)[1] != 0:
Famname.append(i)
Fullname = Fullname + Famname
return Fullname
def findname(Fullname, mydata, crimetitle, words, Lname):
ctitle = initialze()
gname = initialze()
for i in Fullname:
data = mydata
for j in crimetitle:
Flag = 0
try:
# Identifies the Name of the person in government position or proved as innocent in the crime.
# sentence following the name till the crimetitle
forward = mydata.split(i)[1].split(j)[0]
# sentence prior to the name till the crimetitle
backward = mydata.split(i)[0].split(j)[-1]
if backward.split() != []:
# when the length of backward and forward is equal priority is provided to the forward if it's immediately
# followed by a comma or when the backward reaches the first letter of the article.
if mydata.split()[0] == backward.split()[0] or (
len(forward) == len(backward) and forward[0] == ","
):
backward = mydata
try:
# When the forward reaches the last letter priority is given to the backward text.
if mydata.split()[-1] == forward.split()[-1]:
forward = mydata
except:
pass
else:
if mydata.split()[0] == i.split()[0]:
backward = mydata
# Using sentimental analysis to identify the negativity of the text
if (
len(forward) < len(data)
and len(forward) < len(backward)
and senty(forward)[1] == 0
and re.findall("%s" % wrongtitlepattern, forward) == []
):
words = re.findall(r"[\w']+|[.,!?; ]", forward)
# Eliminating the possibility of criminal name getting added to a government title.
for word in words:
if word in Lname:
Flag = 1
if Flag == 0:
data = forward
n = j
# Using sentimental analysis to identify the negativity of the text
if (
len(backward) < len(data)
and len(forward) > len(backward)
and senty(backward)[1] == 0
and re.findall("%s" % wrongtitlepattern, backward) == []
):
words = re.findall(r"[\w']+|[.,!?; ]", backward)
# Eliminating the possibility of criminal name getting added to a government title.
for word in words:
if word in Lname:
Flag = 1
if Flag == 0:
data = backward
n = j
except:
pass
try:
if len(mydata) != len(data):
gname.append(i)
ctitle.append(n)
if len(forward) < len(backward):
mydata = "".join(re.split(i + data + n, mydata))
else:
mydata = "".join(re.split(n + data + i, mydata))
except:
continue
return (ctitle, gname, mydata)
