def analyze_hit_article(analyzer, group, article_set, is_post=False, is_comment=False):
"""
analyze article
:param analyzer: analyzer for analysis
:param group: group object
:param article_set: list of articles
:return string of status
"""
for article in article_set: # better algorithm
if is_post is True:
attach = Attachment.objects.filter(post=article)
if not attach:
if article.message is None:
return "no_message"
words = core.analyze_articles(analyzer, article.message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
if attach[0].description is not None:
message = attach[0].description
elif attach[0].title is not None:
message = attach[0].title
else:
return "no_message"
words = core.analyze_articles(analyzer, message)
add_words_db(group, words, article.like_count, article.comment_count)
elif is_comment is True:
attach = Attachment.objects.filter(comment=article)
if not attach:
if article.message is None:
return "no_message"
words = core.analyze_articles(analyzer, article.message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
if attach[0].description is not None:
message = attach[0].description
elif attach[0].title is not None:
message = attach[0].title
else:
return "no_message"
words = core.analyze_articles(analyzer, message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
return "no_article"
def analyze_hit_article(group, article_set, is_post=False, is_comment=False):
"""
analyze article
:param group: group object
:param article_set: list of articles
:return string of status
"""
for article in article_set: # better algorithm
if is_post is True:
attach = Attachment.objects.filter(post=article)
if not attach:
if article.message is None:
return "no_message"
words = core.analyze_articles(article.message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
if attach[0].description is not None:
message = attach[0].description
elif attach[0].title is not None:
message = attach[0].title
else:
return "no_message"
words = core.analyze_articles(message)
add_words_db(group, words, article.like_count, article.comment_count)
elif is_comment is True:
attach = Attachment.objects.filter(comment=article)
if not attach:
if article.message is None:
return "no_message"
words = core.analyze_articles(article.message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
if attach[0].description is not None:
message = attach[0].description
elif attach[0].title is not None:
message = attach[0].title
else:
return "no_message"
words = core.analyze_articles(message)
add_words_db(group, words, article.like_count, article.comment_count)
else:
return "no_article"
def run_app():
# test-only method lifecoding - 174499879257223, node - 168705546563077, import analysis.spam_app as spam
group = Group.objects.filter(id=168705546563077)[0]
print("name is " + group.name)
a = core.analyze_articles(
analyzer, '$아줌마$상대로섹알바하실분$1일수익80만홈피 $ w w w . y c c 6 6 . c o m !')
print(a)
def delete_spam_sequence(data_object):
"""
analyze deleted spam article from spam list and add spam words to spamwordlist
:param data_object: deleted article object
"""
words = core.analyze_articles(analyzer, data_object.message)
add_spam_words(data_object.group, words)
update_words_level(data_object.group)
def analyze_feed(analyzer, data_object, group):
"""
analyze a feed
:param analyzer: analyzer for analysis
:param data_object: data object
:return: words from analyzed feed
"""
#if data_object.message is not None:
# message = data_object.message
#else:
# message = ''
#if 'attachment' in data_object:
# attach = data_object.get('attachment')
# if 'description' in attach:
# attach_message = attach.get('description')
# elif 'title' in attach:
# attach_message = attach.get('title')
# else:
# attach_message = ''
#else:
# attach_message = ''
message = data_object
attach_message = ''
if message is not '':
message_word_set = core.analyze_articles(analyzer, message)
else:
message_word_set = []
if attach_message is not '':
attach_word_set = core.analyze_articles(analyzer, attach_message)
else:
attach_word_set = []
temp_set = message_word_set + attach_word_set
word_set = list(set(temp_set)) # make better algorithm
word_db = ArchiveAnalysisWord.objects.filter(group=group, weigh__gte=100)
data_set = [sp.word for sp in word_db]
return core.analysis_text_by_words(data_set, word_set)
def delete_spam_sequence(data_object):
"""
analyze deleted spam article from spam list and add spam words to spamwordlist
:param data_object: deleted article object
"""
words = core.analyze_articles(analyzer, data_object.message)
add_spam_words(data_object.group, words)
update_words_level(data_object.group)
def analyze_feed(data_object):
"""
analyze a feed
:param data_object: data object
:return: words from analyzed feed
"""
if data_object.message is not None:
message = data_object.message
else:
message = ''
if 'attachment' in data_object:
attach = data_object.get('attachment')
if 'description' in attach:
attach_message = attach.get('description')
elif 'title' in attach:
attach_message = attach.get('title')
else:
attach_message = ''
else:
attach_message = ''
# message = data_object
# attach_message = ''
if message is not '':
message_word_set = core.analyze_articles(message)
else:
message_word_set = []
if attach_message is not '':
attach_word_set = core.analyze_articles(attach_message)
else:
attach_word_set = []
temp_set = message_word_set + attach_word_set
word_set = list(set(temp_set)) # make better algorithm
word_db = MonthlyWords.objects.filter(group=data_object.group)
data_set = [sp.word for sp in word_db]
return core.analysis_text_by_words(data_set, word_set, 5)
def analyze_feed(data_object):
"""
analyze a feed
:param data_object: data object
:return: words from analyzed feed
"""
if data_object.message is not None:
message = data_object.message
else:
message = ""
if "attachment" in data_object:
attach = data_object.get("attachment")
if "description" in attach:
attach_message = attach.get("description")
elif "title" in attach:
attach_message = attach.get("title")
else:
attach_message = ""
else:
attach_message = ""
# message = data_object
# attach_message = ''
if message is not "":
message_word_set = core.analyze_articles(message)
else:
message_word_set = []
if attach_message is not "":
attach_word_set = core.analyze_articles(attach_message)
else:
attach_word_set = []
temp_set = message_word_set + attach_word_set
word_set = list(set(temp_set)) # make better algorithm
word_db = MonthlyWords.objects.filter(group=data_object.group)
data_set = [sp.word for sp in word_db]
return core.analysis_text_by_words(data_set, word_set, 5)
def analyze_feed_spam(analyzer, group, message):
"""
Return true if analyzed words and spam words are same
:param analyzer: analyzer for analysis
:param group: group object
:param message: message of post or comment
:return: true or false
"""
spam_db = SpamWordList.objects.filter(Q(group=group), Q(status='filter') | Q(status='user'))
data_set = [sp.word for sp in spam_db]
word_set = core.analyze_articles(analyzer, message)
return core.analysis_text_by_words(data_set, word_set)
def run_app():
print('hello')
message = '초보자들 헷갈리라고 넣는 겁니다 (웃음). 지금의 단계에서는 굳이 이해하려고 하실 필요가 없습니다. 꼭 알고 싶으시다면 "변수는 정보와 실체의 합으로서 존재하는데, test는 변수에 담긴 정보를 의미하고 &test는 변수의 실체를 의미한다" 정도로 생각해 두시면 되겠습니다. 더 쉽게 말하면, int b = 72; 이렇게 값이 72인 변수 b가 있을 때, 우리는 이것을 b라는 상자에 72라는 정보가 담겨 있는 것으로 이해할 수 있습니다. b라고 지정하면 72라는 값을 꺼내 주는 것이고, &b라고 지정하면 72가 담긴 상자를 통째로 건네주는 것입니다. 내용물을 꺼내서 주면 받는 쪽에서는 상자에 접근할 수 없으므로 상자의 내용물을 바꿀 수는 없습니다. 상자를 통째로 주면 받는 쪽에서 상자에 다른 물건을 담아놓을 수 있겠죠.'
core.analyze_articles(message)
message = '우려했던 K-알파고가 곧 나올듯 합니다.... :('
core.analyze_articles(message)
message = '역시 ..LG'
core.analyze_articles(message)
def run_app():
print("hello")
message = '초보자들 헷갈리라고 넣는 겁니다 (웃음). 지금의 단계에서는 굳이 이해하려고 하실 필요가 없습니다. 꼭 알고 싶으시다면 "변수는 정보와 실체의 합으로서 존재하는데, test는 변수에 담긴 정보를 의미하고 &test는 변수의 실체를 의미한다" 정도로 생각해 두시면 되겠습니다. 더 쉽게 말하면, int b = 72; 이렇게 값이 72인 변수 b가 있을 때, 우리는 이것을 b라는 상자에 72라는 정보가 담겨 있는 것으로 이해할 수 있습니다. b라고 지정하면 72라는 값을 꺼내 주는 것이고, &b라고 지정하면 72가 담긴 상자를 통째로 건네주는 것입니다. 내용물을 꺼내서 주면 받는 쪽에서는 상자에 접근할 수 없으므로 상자의 내용물을 바꿀 수는 없습니다. 상자를 통째로 주면 받는 쪽에서 상자에 다른 물건을 담아놓을 수 있겠죠.'
core.analyze_articles(message)
message = "우려했던 K-알파고가 곧 나올듯 합니다.... :("
core.analyze_articles(message)
message = "역시 ..LG"
core.analyze_articles(message)
def analyze_feed_spam(group, message):
"""
Return true if analyzed words and spam words are same
:param group: group object
:param message: message of post or comment
:return: true or false
"""
spam_db = SpamWordList.objects.filter(
Q(group=group),
Q(status='filter') | Q(status='user'))
data_set = [sp.word for sp in spam_db]
word_set = core.analyze_articles(analyzer, message)
# arg = SpamWordList.objects.filter(group=group).aggregate(avgcount=Avg('count'))
return core.analysis_text_by_words(data_set, word_set, 0)
def weekly_analyze_feed(group):
"""
analyze monthly feed
:param group: group object
:return: if works well return true
"""
now = timezone.now()
time = now - timezone.timedelta(days=40)
# print(time)
if MonthlyWords.objects.filter(group=group).exists():
# print('part1')
groupobjcet = MonthlyWords.objects.filter(group=group)[0]
rd = rdelta.relativedelta(now, groupobjcet.lastfeeddate)
# print(rd.days)
if groupobjcet is None or rd.days > 1:
MonthlyWords.objects.filter(group=group).delete()
post = Post.objects.filter(group=group, created_time__range=(time, now))
if post.count() == 0:
post = None
comment = Comment.objects.filter(group=group, created_time__range=(time, now))
if comment.count() == 0:
comment = None
if post and comment is None:
return False
else:
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (p.like_count + p.comment_count)
else:
post_dict[w] = 0
if comment is not None:
for c in comment:
words = core.analyze_articles(c.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (c.like_count + c.comment_count)
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value/total_words*2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthlyWords(group=group, lastfeeddate=now, word=wd[0], weigh=wd[1]).save()
return True
else:
return False
else:
post = Post.objects.filter(group=group, created_time__range=(time, now))
if post.count() == 0:
post = None
comment = Comment.objects.filter(group=group, created_time__range=(time, now))
if comment.count() == 0:
comment = None
print(post.count())
print(comment.count())
if post and comment is None:
return False
else:
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (p.like_count + p.comment_count)
else:
post_dict[w] = 0
if comment is not None:
for c in comment:
words = core.analyze_articles(c.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (c.like_count + c.comment_count)
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value/total_words*2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthlyWords(group=group, lastfeeddate=now, word=wd[0], weigh=wd[1]).save()
return True
def analyze_monthly_post(group, from_date, to_date):
"""
Make trend words
:param group: group object
:param from_date: collect data from this date
:param to_date: collect data to this date
:return: if works well return true
"""
if MonthTrendWord.objects.filter(group=group).exists():
try:
group_objcet = MonthTrendWord.objects.filter(group=group, datedtime__range=(from_date, to_date))[0]
except IndexError:
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (p.like_count + p.comment_count)
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value/total_words*2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date).save()
return True
rd = rdelta.relativedelta(to_date, group_objcet.datedtime)
if rd.days < 30:
rdt = rdelta.relativedelta(to_date, group_objcet.lastfeeddate)
if rdt.days > 1:
MonthTrendWord.objects.filter(group=group, datedtime__range=(from_date, to_date)).delete()
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (p.like_count + p.comment_count)
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value/total_words*2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date).save()
return True
else:
return False
else:
return False
else:
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += (p.like_count + p.comment_count)
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value/total_words*2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date).save()
return True
def analyze_monthly_post(group, from_date, to_date):
"""
Make trend words
:param group: group object
:param from_date: collect data from this date
:param to_date: collect data to this date
:return: if works well return true
"""
if MonthTrendWord.objects.filter(group=group).exists():
try:
group_objcet = MonthTrendWord.objects.filter(group=group, datedtime__range=(from_date, to_date))[0]
except IndexError:
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += p.like_count + p.comment_count
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value / total_words * 2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date).save()
return True
rd = rdelta.relativedelta(to_date, group_objcet.datedtime)
if rd.days < 30:
rdt = rdelta.relativedelta(to_date, group_objcet.lastfeeddate)
if rdt.days > 1:
MonthTrendWord.objects.filter(group=group, datedtime__range=(from_date, to_date)).delete()
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += p.like_count + p.comment_count
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value / total_words * 2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(
group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date
).save()
return True
else:
return False
else:
return False
else:
post = Post.objects.filter(group=group, created_time__range=(from_date, to_date))
if post.count() == 0:
return False
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += p.like_count + p.comment_count
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value / total_words * 2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthTrendWord(group=group, datedtime=from_date, word=wd[0], weigh=wd[1], lastfeeddate=to_date).save()
return True
def weekly_analyze_feed(group):
"""
analyze monthly feed
:param group: group object
:return: if works well return true
"""
now = timezone.now()
time = now - timezone.timedelta(days=40)
# print(time)
if MonthlyWords.objects.filter(group=group).exists():
# print('part1')
groupobjcet = MonthlyWords.objects.filter(group=group)[0]
rd = rdelta.relativedelta(now, groupobjcet.lastfeeddate)
# print(rd.days)
if groupobjcet is None or rd.days > 1:
MonthlyWords.objects.filter(group=group).delete()
post = Post.objects.filter(group=group, created_time__range=(time, now))
if post.count() == 0:
post = None
comment = Comment.objects.filter(group=group, created_time__range=(time, now))
if comment.count() == 0:
comment = None
if post and comment is None:
return False
else:
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += p.like_count + p.comment_count
else:
post_dict[w] = 0
if comment is not None:
for c in comment:
words = core.analyze_articles(c.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += c.like_count + c.comment_count
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value / total_words * 2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthlyWords(group=group, lastfeeddate=now, word=wd[0], weigh=wd[1]).save()
return True
else:
return False
else:
post = Post.objects.filter(group=group, created_time__range=(time, now))
if post.count() == 0:
post = None
comment = Comment.objects.filter(group=group, created_time__range=(time, now))
if comment.count() == 0:
comment = None
print(post.count())
print(comment.count())
if post and comment is None:
return False
else:
post_dict = {}
if post is not None:
for p in post:
words = core.analyze_articles(p.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += p.like_count + p.comment_count
else:
post_dict[w] = 0
if comment is not None:
for c in comment:
words = core.analyze_articles(c.message)
for w in words:
if post_dict.get(w) is not None:
post_dict[w] += c.like_count + c.comment_count
else:
post_dict[w] = 0
total_words = 1
count_value = 1
for i in post_dict.values():
if i != 0:
total_words += 1
count_value += i
avg = int(count_value / total_words * 2)
refinewords = []
for i in post_dict.items():
if i[1] > avg:
refinewords.append((i[0], i[1]))
for wd in refinewords:
MonthlyWords(group=group, lastfeeddate=now, word=wd[0], weigh=wd[1]).save()
return True
def run_app():
# test-only method lifecoding - 174499879257223, node - 168705546563077, import analysis.spam_app as spam
group = Group.objects.filter(id=168705546563077)[0]
print("name is "+group.name)
a = core.analyze_articles(analyzer, '$아줌마$상대로섹알바하실분$1일수익80만홈피 $ w w w . y c c 6 6 . c o m !')
print(a)
