def main():
id = 1
for topic in TOPICS:
path = PATH + topic
directory = os.fsencode(path)
for file in os.listdir(directory):
filename = os.fsdecode(file)
if filename.endswith(".json"):
data = json.load(open(path + '/' + filename))
data.pop('localpath', None)
data.pop('date_download', None)
data.pop('date_modify', None)
data.pop('filename', None)
data["id"] = id
id += 1
for content in CONTENTS:
if content in data.keys() and data[content] != None:
new_content = word_tokenize(data[content])
data[content] = new_content
with open(path + '/' + filename, 'w') as outfile:
json.dump(data, outfile)
def get_stats(self, generator, arto=False,format_pandas=False):
""""""
data = defaultdict(list)
i = 0
for t in generator:
i += 1
ambig1 = t.ambiguity(stress_polysyll=False)
ambig2 = t.ambiguity(stress_polysyll=True)
tree1 = t.max_stress_disambiguate()[0]
tree1.set_pstress()
tree1.set_stress()
tree2a = t.min_stress_disambiguate(stress_polysyll=True)[0]
tree2a.set_pstress()
tree2a.set_stress()
tree2b = t.min_stress_disambiguate(stress_polysyll=False)[0]
tree2b.set_pstress()
tree2b.set_stress()
j = 0
preterms1 = list(tree1.preterminals())
min1 = float(min([preterm.stress() for preterm in preterms1 if not np.isnan(preterm.stress())]))
max1 = max([preterm.stress() for preterm in preterms1 if not np.isnan(preterm.stress())]) - min1
preterms2a = list(tree2a.preterminals())
min2a = float(min([preterm.stress() for preterm in preterms2a if not np.isnan(preterm.stress())]))
max2a = max([preterm.stress() for preterm in preterms2a if not np.isnan(preterm.stress())]) - min2a
preterms2b = list(tree2b.preterminals())
min2b = float(min([preterm.stress() for preterm in preterms2b if not np.isnan(preterm.stress())]))
max2b = max([preterm.stress() for preterm in preterms2b if not np.isnan(preterm.stress())]) - min2b
preterms_raw = list(t.preterminals())
minmean = float(min([np.mean([preterm1.stress(), preterm2a.stress(), preterm2b.stress()]) for preterm1, preterm2a, preterm2b in zip(preterms1, preterms2a, preterms2b) if not np.isnan(preterm1.stress())]))
maxmean = max([np.mean([preterm1.stress(), preterm2a.stress(), preterm2b.stress()]) for preterm1, preterm2a, preterm2b in zip(preterms1, preterms2a, preterms2b) if not np.isnan(preterm1.stress())]) - minmean
sent = ' '.join([preterm[0] for preterm in preterms_raw])
sentlen = len(preterms_raw)
for preterm1, preterm2a, preterm2b, preterm_raw in zip(preterms1, preterms2a, preterms2b, preterms_raw):
j += 1
data['widx'].append(j)
data['norm_widx'].append(float(j) / sentlen if sentlen else np.nan)
data['word'].append(preterm1[0])
if preterm_raw._lstress == 0:
data['lexstress'].append('yes')
elif preterm_raw._lstress == -.5:
data['lexstress'].append('ambig')
elif preterm_raw._lstress == -1:
data['lexstress'].append('no')
else:
data['lexstress'].append('???')
data['seg'].append(' '.join(preterm1.seg()))
data['nseg'].append(preterm1.nseg())
data['nsyll'].append(preterm1.nsyll())
data['nstress'].append(preterm1.nstress())
data['pos'].append(preterm1.category())
data['dep'].append(preterm1.dependency())
if arto:
data['m1'].append(-(preterm1.stress()-1))
data['m2a'].append(-(preterm2a.stress()-1))
data['m2b'].append(-(preterm2b.stress()-1))
data['mean'].append(-(np.mean([preterm1.stress(), preterm2a.stress(), preterm2b.stress()])-1))
else:
data['m1'].append(preterm1.stress())
data['m2a'].append(preterm2a.stress())
data['m2b'].append(preterm2b.stress())
data['mean'].append(np.mean([preterm1.stress(), preterm2a.stress(), preterm2b.stress()]))
data['norm_m1'].append((preterm1.stress()-min1)/max1 if max1 else np.nan)
data['norm_m2a'].append((preterm2a.stress()-min2a)/max2a if max2a else np.nan)
data['norm_m2b'].append((preterm2b.stress()-min2b)/max2b if max2b else np.nan)
data['norm_mean'].append((np.mean([preterm1.stress(), preterm2a.stress(), preterm2b.stress()])-minmean)/maxmean if maxmean else np.nan)
data['sidx'].append(i)
data['sent'].append(sent)
data['ambig_words'].append(ambig1)
data['ambig_monosyll'].append(ambig2)
data['contour'].extend([' '.join(str(x) for x in data['mean'][-(j):])]*j)
if format_pandas:
for k, v in data.iteritems():
data[k] = pd.Series(v)
df=pd.DataFrame(data, columns=['widx', 'norm_widx', 'word', 'seg', 'lexstress',
'nseg', 'nsyll', 'nstress',
'pos', 'dep',
'm1', 'm2a', 'm2b', 'mean',
'norm_m1', 'norm_m2a', 'norm_m2b', 'norm_mean',
'sidx', 'sent', 'ambig_words', 'ambig_monosyll',
'contour'])
return df
keys=data.keys()
old=[]
num_rows=len(data[keys[0]])
for i_row in range(num_rows):
dx={}
for k in keys:
dx[k]=data[k][i_row]
old+=[dx]
return old
def get_stats(self, generator, arto=False, format_pandas=False):
""""""
data = defaultdict(list)
i = 0
for t in generator:
i += 1
ambig1 = t.ambiguity(stress_polysyll=False)
ambig2 = t.ambiguity(stress_polysyll=True)
tree1 = t.max_stress_disambiguate()[0]
tree1.set_pstress()
tree1.set_stress()
tree2a = t.min_stress_disambiguate(stress_polysyll=True)[0]
tree2a.set_pstress()
tree2a.set_stress()
tree2b = t.min_stress_disambiguate(stress_polysyll=False)[0]
tree2b.set_pstress()
tree2b.set_stress()
j = 0
preterms1 = list(tree1.preterminals())
min1 = float(
min([
preterm.stress() for preterm in preterms1
if not np.isnan(preterm.stress())
]))
max1 = max([
preterm.stress()
for preterm in preterms1 if not np.isnan(preterm.stress())
]) - min1
preterms2a = list(tree2a.preterminals())
min2a = float(
min([
preterm.stress() for preterm in preterms2a
if not np.isnan(preterm.stress())
]))
max2a = max([
preterm.stress()
for preterm in preterms2a if not np.isnan(preterm.stress())
]) - min2a
preterms2b = list(tree2b.preterminals())
min2b = float(
min([
preterm.stress() for preterm in preterms2b
if not np.isnan(preterm.stress())
]))
max2b = max([
preterm.stress()
for preterm in preterms2b if not np.isnan(preterm.stress())
]) - min2b
preterms_raw = list(t.preterminals())
minmean = float(
min([
np.mean([
preterm1.stress(),
preterm2a.stress(),
preterm2b.stress()
]) for preterm1, preterm2a, preterm2b in zip(
preterms1, preterms2a, preterms2b)
if not np.isnan(preterm1.stress())
]))
maxmean = max([
np.mean([
preterm1.stress(),
preterm2a.stress(),
preterm2b.stress()
]) for preterm1, preterm2a, preterm2b in zip(
preterms1, preterms2a, preterms2b)
if not np.isnan(preterm1.stress())
]) - minmean
sent = ' '.join([preterm[0] for preterm in preterms_raw])
sentlen = len(preterms_raw)
for preterm1, preterm2a, preterm2b, preterm_raw in zip(
preterms1, preterms2a, preterms2b, preterms_raw):
j += 1
data['widx'].append(j)
data['norm_widx'].append(
float(j) / sentlen if sentlen else np.nan)
data['word'].append(preterm1[0])
if preterm_raw._lstress == 0:
data['lexstress'].append('yes')
elif preterm_raw._lstress == -.5:
data['lexstress'].append('ambig')
elif preterm_raw._lstress == -1:
data['lexstress'].append('no')
else:
data['lexstress'].append('???')
data['seg'].append(' '.join(preterm1.seg()))
data['nseg'].append(preterm1.nseg())
data['nsyll'].append(preterm1.nsyll())
data['nstress'].append(preterm1.nstress())
data['pos'].append(preterm1.category())
data['dep'].append(preterm1.dependency())
if arto:
data['m1'].append(-(preterm1.stress() - 1))
data['m2a'].append(-(preterm2a.stress() - 1))
data['m2b'].append(-(preterm2b.stress() - 1))
data['mean'].append(-(np.mean([
preterm1.stress(),
preterm2a.stress(),
preterm2b.stress()
]) - 1))
else:
data['m1'].append(preterm1.stress())
data['m2a'].append(preterm2a.stress())
data['m2b'].append(preterm2b.stress())
data['mean'].append(
np.mean([
preterm1.stress(),
preterm2a.stress(),
preterm2b.stress()
]))
data['norm_m1'].append((preterm1.stress() - min1) /
max1 if max1 else np.nan)
data['norm_m2a'].append((preterm2a.stress() - min2a) /
max2a if max2a else np.nan)
data['norm_m2b'].append((preterm2b.stress() - min2b) /
max2b if max2b else np.nan)
data['norm_mean'].append((np.mean([
preterm1.stress(),
preterm2a.stress(),
preterm2b.stress()
]) - minmean) / maxmean if maxmean else np.nan)
data['sidx'].append(i)
data['sent'].append(sent)
data['ambig_words'].append(ambig1)
data['ambig_monosyll'].append(ambig2)
data['contour'].extend(
[' '.join(str(x) for x in data['mean'][-(j):])] * j)
if format_pandas:
for k, v in data.items():
data[k] = pd.Series(v)
df = pd.DataFrame(data,
columns=[
'widx', 'norm_widx', 'word', 'seg',
'lexstress', 'nseg', 'nsyll', 'nstress',
'pos', 'dep', 'm1', 'm2a', 'm2b', 'mean',
'norm_m1', 'norm_m2a', 'norm_m2b',
'norm_mean', 'sidx', 'sent', 'ambig_words',
'ambig_monosyll', 'contour'
])
return df
keys = list(data.keys())
old = []
num_rows = len(data[keys[0]])
for i_row in range(num_rows):
dx = {}
for k in keys:
dx[k] = data[k][i_row]
old += [dx]
return old
def genThesis(topic):
"""
in order to generate the thesis, we need the following:
- title
- url
- rating = opinion
- thesis: the bold text for the yes or no section
** the thesis has to contain some keywords from the title
- support: the sentence following the yes or no bold statement
"""
title, url, category = genTopic(topic)
# print '\n'
# print title
# print '===================================================== \n'
r = requests.get(url)
data = r.text
soup = BeautifulSoup(data)
cleaned = []
""" remove stopwords using nltk stop list and print the keywords """
# stoppers = stopWords()
keywords = [w for w in title.lower().split() if not w in stopwords.words("english")]
# print "keywords: ", keywords
for i in keywords:
cleaned.append(dePunc(i))
# print "cleaned: ", cleaned
# cleaned list of keywords
keys = ' '.join(cleaned)
vote = soup.find("span", "no-text").text
strings = str(vote).split()
rating = int(strings[0][:-1]) # this is the 'no' rating
opinions = []
args = []
argSplit = []
clean_keys = []
data = {}
""" for each top argument, check if it is long enough and contains more than one word from
the list of title keywords. """
if rating > 50:
# vote is " No"
args = soup.find('div', attrs={'id': 'no-arguments'}).find_all("li", "hasData")
# print args
for i in range(0, len(args)):
# find the list items
temp = args[i].find("h2").text
userArg = args[i].find("p").text
# print userArg
tmps = temp.split()
# print tmps
for i in tmps:
clean_keys.append(dePunc(i))
if len(clean_keys) > 3:
count = 0
for i in clean_keys:
for j in cleaned:
# print stemmer.lemmatize(i).lower()+','+stemmer.lemmatize(j).lower()
if stemmer.lemmatize(i).lower() == stemmer.lemmatize(j).lower():
count += 1
if count > 1:
# opinions.append(' '.join(tmps))
# data[str(temp).encode('utf8')] = str(userArg).encode('utf8')
data[u''.join(temp).encode('utf-8')] = u''.join(userArg).encode('utf-8') #try to solve ASCII error
# print count
else:
# vote is " Yes"
args = soup.find('div', attrs={'id': 'yes-arguments'}).find_all("li", "hasData")
# print args
for i in range(0, len(args)):
# find the list items
temp = args[i].find("h2").text
userArg = args[i].find("p").text
# print userArg
tmps = temp.split()
# print tmps
for i in tmps:
clean_keys.append(dePunc(i))
if len(clean_keys) > 3:
# opinions.append(' '.join(tmps))
# print opinions
count = 0
for i in clean_keys:
for j in cleaned:
# print stemmer.lemmatize(i).lower()+','+stemmer.lemmatize(j).lower()
if stemmer.lemmatize(i).lower() == stemmer.lemmatize(j).lower():
count += 1
if count > 1:
# opinions.append(temp)
data[temp.encode('utf8')] = userArg.encode('utf8')
# print count
""" form the thesis by taking a random opinion and it's supporting argument """
# long_support = userArg
# print data
# if not opinions: #checking if opinions is empty
# print "Couldn't find anything - opinions"
# # return "", ""
# elif not userArg: #checking if userArgs is empty
# print "Couldn't find anything - arguments"
# # return "", ""
# else: #if they aren't empty, do this
# # print 'Top Argument: '+opinions[0]+'\n'
# # topArg = opinions[0].split()
# """ send the thesis and userArgs off to the function to be strengthened """
# # thesis = opinions[0]+' '+long_support
# thesis_stmt = thesis(arg, long_support)
# print "Thesis: "
# print thesis
# # return title, thesis
if not data:
# print "Couldn't find anything - data dictionary"
return "", "", "", ""
else: #if they aren't empty, do this
# print 'Top Argument: '+opinions[0]+'\n'
# topArg = opinions[0].split()
""" send the thesis and userArgs off to the function to be strengthened """
# thesis = opinions[0]+' '+long_support
# thesis_stmt = improvements(title, data)
# print "Thesis: "
# print thesis
one = random.choice(data.keys())
two = data[one]
if two == "":
return "", "", "", ""
else:
punct = ['!', '?']
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
tokens = tokenizer.tokenize(two.strip())
# for token in tokens:
# print token, "\n"
thesis_len = len(tokens)
support = ' '.join(tokenizer.tokenize(two.strip()))
while thesis_len > 5:
one = random.choice(data.keys())
two = data[one]
tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
thesis_len = len(tokenizer.tokenize(two.strip()))
support = ' '.join(tokenizer.tokenize(two.strip()))
# print "support: ", support
if tokens[0] == one:
one = ""
if one[-1] == '.':
one = one
elif one[-1] in punct or one[-1] not in punct:
one = one + '.'
# print one
thesis = one + " " + support
# print title
# print 'Thesis: ', thesis
return title, one, support, url
def facebook():
connect = app.config['SOCIALMEDIADATA_COLLECTION']
user_obj = session.get('user', None)
hotel = user_obj['hotel']
place = user_obj['Place']
data = list(connect.find({'hotel': hotel, 'Place': place}))
fbkey = data[0]['fbkeyword']
if fbkey != "":
url = "https://graph.facebook.com/{0}/?fields=fan_count,talking_about_count,rating_count,checkins,overall_star_rating,feed.limit(100){{name,message,picture,type,link,likes.limit(0).summary(true),shares,comments.limit(0).summary(true)}},picture&access_token={1}".format(
fbkey, app.config["FB_TOKEN"])
#r = requests.get(url, headers=headers)
r = requests.get(url)
data = json.loads((r.text))
JSON = []
total_fb = 0
total_fb_pos = 0
total_fb_neg = 0
total_fb_neu = 0
if 'error' in data.keys():
raise abort(500)
for d in data["feed"]["data"]:
total_fb = total_fb + 1
cur = {}
if "name" in d:
cur['name'] = str(d['name'])
else:
cur['name'] = None
if "picture" in d:
cur['picture'] = str(d['picture'])
else:
cur['picture'] = None
if "link" in d:
cur['link'] = str(d['link'])
else:
cur['link'] = None
if 'message' in d:
cur['message'] = str(d['message'])
else:
cur['message'] = None
if 'shares' in d:
cur['shares'] = d['shares']['count']
else:
cur['shares'] = 0
if 'likes' in d:
cur['total_likes'] = d['likes']['summary']['total_count']
else:
cur['total_likes'] = 0
if 'comments' in d:
cur['total_comments'] = d['comments']['summary']['total_count']
else:
cur['total_comments'] = 0
if 'message' in d:
cur['sentiment'] = str(sentiment_analyzer(str(d['message'])))
else:
cur['sentiment'] = None
if cur['sentiment'] == 'Positive':
total_fb_pos = total_fb_pos + 1
elif cur['sentiment'] == 'Neutral':
total_fb_neu = total_fb_neu + 1
else:
total_fb_neg = total_fb_neg + 1
JSON.append({
'name': cur['name'],
'picture': cur['picture'],
"link": cur['link'],
'message': cur['message'],
'shares': cur['shares'],
'total_likes': cur['total_likes'],
'total_comments': cur['total_comments'],
'sentiment': cur['sentiment']
})
data = [{
"data": JSON,
"user_picture": data['picture'],
"fan_count": data['fan_count'],
"talking_about_count": data['talking_about_count'],
"rating_count": data['rating_count'],
"checkins": data['checkins'],
"overall_star_rating": data['overall_star_rating'],
"socialchannel": "Facebook",
"totalFb": {
'total_fb': total_fb,
'total_fb_pos': total_fb_pos,
'total_fb_neu': total_fb_neu,
'total_fb_neg': total_fb_neg
},
"imgPath": "/static/images/fb.png"
}]
else:
data = [{
"socialchannel": "Facebook",
"totalFb": {
'total_fb': 0,
'total_fb_pos': 0,
'total_fb_neu': 0,
'total_fb_neg': 0
},
"fan_count": 0,
"talking_about_count": 0,
"checkins": 0,
"imgPath": "/static/images/fb.png"
}]
return data