# Negative News Articles from 1 to 9 JSON files
with open(fileName) as json_data:
d = json.load(json_data)
rn = d["posts"]
# go through all posts
for post in rn:
p = post["published"]
m = int(p[5:7])
if m == 11:
d = int(p[8:10])
else:
d = int(p[8:10]) + 30
an[d - day] += 1
social_impact = getSocialData(post)
an_social[d - day] += social_impact
#text = post["text"]
#text = text.encode('ascii','ignore')
#foundindx = text.findall("Google")
#print "Relevant Words: " + str(foundindx)
#print text
out0 = open('negative.txt', 'w')
out0.truncate()
for i in an:
out0.write(str(i))
out0.write("\n")
out0.close()
def parseData(negativeFileName, postiveFileName, neutralFileName, numNegativ,
numPositive, numNeutral):
days_back = 30
date_days_ago = datetime.now() - timedelta(days=days_back)
day = datetime.today().day
day = 7
# array to track number of positive articles by day
ap = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
ap_social = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
ap_trump = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
ap_clinton = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
ap_election = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
# array to track number of positive articles by day
an = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
an_social = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
an_trump = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
an_clinton = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
an_election = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
# array to track number of positive articles by day
aa = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
aa_social = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
aa_trump = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
aa_clinton = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
aa_election = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0
]
for x in range(1, numNegativ + 1):
fileName = negativeFileName + str(x) + '.json'
print fileName
# Negative News Articles from 1 to 9 JSON files
with open(fileName) as json_data:
d = json.load(json_data)
rn = d["posts"]
# go through all posts
for post in rn:
p = post["published"]
m = int(p[5:7])
if m == 11:
d = int(p[8:10])
else:
d = int(p[8:10]) + 30
an[d - day] += 1
social_impact = getSocialData(post)
an_social[d - day] += social_impact
# Get text from news object
text = post["text"]
text = text.encode('ascii', 'ignore')
# Search for Trump
foundindx = text.find("Trump")
if foundindx > 0:
an_trump[d - day] += 1
# Search for Election
foundindx = text.find("election")
if foundindx > 0:
an_election[d - day] += 1
# Search for Election
foundindx = text.find("Clinton")
if foundindx > 0:
an_clinton[d - day] += 1
print "Found Trump: " + str(an_trump)
print "Found Election: " + str(an_election)
print "Found Election: " + str(an_clinton)
out0 = open('negative.txt', 'w')
out0.truncate()
for i in an:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('negative_social.txt', 'w')
out0.truncate()
for i in an_social:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('negative_election.txt', 'w')
out0.truncate()
for i in an_election:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('negative_trump.txt', 'w')
out0.truncate()
for i in an_trump:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('negative_clinton.txt', 'w')
out0.truncate()
for i in an_clinton:
out0.write(str(i))
out0.write("\n")
out0.close()
for x in range(1, numNeutral + 1):
fileName = neutralFileName + str(x) + '.json'
print fileName
# calculate total number of neutral articles by day
with open(fileName) as json_data:
d = json.load(json_data)
ra = d["posts"]
for post in ra:
p = post["published"]
m = int(p[5:7])
if m == 11:
d = int(p[8:10])
else:
d = int(p[8:10]) + 30
aa[d - day] += 1
social_impact = getSocialData(post)
an_social[d - day] += social_impact
# Get text from news object
text = post["text"]
text = text.encode('ascii', 'ignore')
# Search for Trump
foundindx = text.find("Trump")
if foundindx > 0:
aa_trump[d - day] += 1
# Search for Election
foundindx = text.find("election")
if foundindx > 0:
aa_election[d - day] += 1
# Search for Election
foundindx = text.find("Clinton")
if foundindx > 0:
aa_clinton[d - day] += 1
print "Found Trump: " + str(aa_trump)
print "Found Election: " + str(aa_election)
print "Found Clinton: " + str(aa_clinton)
out0 = open('all.txt', 'w')
out0.truncate()
for i in aa:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('all_social.txt', 'w')
out0.truncate()
for i in aa_social:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('all_election.txt', 'w')
out0.truncate()
for i in aa_election:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('all_trump.txt', 'w')
out0.truncate()
for i in aa_trump:
out0.write(str(i))
out0.write("\n")
out0.close()
out0 = open('all_clinton.txt', 'w')
out0.truncate()
for i in aa_clinton:
out0.write(str(i))
out0.write("\n")
out0.close()
for x in range(1, numPositive + 1):
fileName = postiveFileName + str(x) + '.json'
print fileName
# calculates number of positive articles per day, from the last 30 days
with open(fileName) as json_data:
d = json.load(json_data)
rp = d["posts"]
for post in rp:
p = post["published"]
m = int(p[5:7])
if m == 11:
d = int(p[8:10])
else:
d = int(p[8:10]) + 30
ap[d - day] += 1
social_impact = getSocialData(post)
an_social[d - day] += social_impact
# Get text from news object
text = post["text"]
text = text.encode('ascii', 'ignore')
# Search for Trump
foundindx = text.find("Trump")
if foundindx > 0:
ap_trump[d - day] += 1
# Search for Election
foundindx = text.find("election")
if foundindx > 0:
ap_election[d - day] += 1
# Search for Election
foundindx = text.find("Clinton")
if foundindx > 0:
ap_clinton[d - day] += 1
print "Found Trump: " + str(ap_trump)
print "Found Election: " + str(ap_election)
print "Found Clinton: " + str(ap_clinton)
out1 = open('positive.txt', 'w')
out1.truncate()
for i in ap:
out1.write(str(i))
out1.write("\n")
out1.close()
out1 = open('positive_social.txt', 'w')
out1.truncate()
for i in ap_social:
out1.write(str(i))
out1.write("\n")
out1.close()
out1 = open('positive_election.txt', 'w')
out1.truncate()
for i in ap_election:
out1.write(str(i))
out1.write("\n")
out1.close()
out1 = open('positive_trump.txt', 'w')
out1.truncate()
for i in ap_trump:
out1.write(str(i))
out1.write("\n")
out1.close()
out1 = open('positive_clinton.txt', 'w')
out1.truncate()
for i in ap_clinton:
out1.write(str(i))
out1.write("\n")
out1.close()