def anime(id):
db = Database(db_name)
title = db.read('anime_list', ['judul'],
' WHERE id=' + str(id))[0][0]
opinion = db.read('opinion_orientation',
['feature_name', 'good', 'bad'],
' WHERE id_anime=' + str(id))
return render_template('opinion.html',
title=title,
opinions=opinion)
def feed(page):
c = Crawler()
c.set_url('https://fs.blog/blog/page/' + str(page))
db = Database(db_name)
# Ini method untuk mengambil artikel paling paru
titles = c.get_feed()
# Pengecekan apakah ada artikel yang sudah disimpan
# Karena ketika artikel sudah disimpan, maka link akan mengarah ke web seite tersimpan saja
title_status = {}
for title in titles.keys():
data = db.read('articles', ['id', 'title'],
' WHERE title="' + title + '"')
if len(data) > 0:
title_status[title] = data[0][0]
else:
title_status[title] = titles[title]
return render_template('feed.html',
titles=titles,
title_status=title_status,
page=page)
def article(article_id):
db = Database(db_name)
data = db.read('articles', ['title', 'raw_article'],
' WHERE id=' + str(article_id))
return render_template('article.html', article=data)
def index():
db = Database(db_name)
row = db.read('articles', ['id', 'title'])
return render_template('index.html', data_title=row)
class Review:
# First config for read
column = ['sentences'] # column
table_name = 'sentences' # table name
'''
This class provide interface to extract the feature and get the opinion text. This is our main process
'''
def __init__(self, file_name, id_anime, feature=[]):
self.feature_list = set(feature) # Our feature list
self.noun_list = [
] # Our noun list use for transaction and mining the frequent feature
self.id_anime = id_anime
self.db = Database(file_name) # Our databse interface
data = self.db.read(self.table_name, self.column,
' WHERE anime_id=' + id_anime)
self.sentences = []
# The review is consist a sentence class
# You can look at the sentence class to see what can a sentence do include post tagging, tokenizing and so on
for row in data:
self.sentences.append(Sentence(row[0].lower(), 5114))
self.feature_result = []
'''
This method provide an interface to extract the feature list. The process is like this
- Use the post tagging to find the noun or a feature candidate.
- Save to the noun list
- Use assosiation mining to find the frequent feature
- Save to the feature list
'''
def extract_feature_list(self):
if len(self.feature_list) == 0:
# Get the noun for each sentences
for sentence in self.sentences:
for word in sentence.tagged:
if word[1] == 'NN':
sentence.noun.append(word[0])
# If the sentences didn't countain noun, it's just a dump sentences
if len(sentence.noun) > 0:
sentence.opinion = True
self.noun_list.append(sentence.noun)
else:
sentence.opinion = False
# Use apriori method to mine the feature
association_rules = apriori(self.noun_list,
min_support=0.001,
min_confidence=0,
min_lift=3,
min_length=2)
association_results = list(association_rules)
features = []
for item in association_results:
pair = item[0]
items = [x for x in pair]
self.feature_list.add(items[0])
'''
After mine the feature list we mine the opinion about that feature.
- First, we get the adjective that most nearby to the noun
- We categorize them with positive or negative with wordnet
'''
def extract_feature_opinion(self):
# List all the feature and make them the key to the dictionary
feature_count = {}
for feature in self.feature_list:
feature_count[feature] = []
# Find all feature and opinion in all sentences
for sentence in self.sentences:
if sentence.opinion == True:
all_words = len(sentence.tagged)
for i, j in enumerate(sentence.tagged):
# Find the nearby adjective forward or backward
if j[1] == 'NN' and j[0] in self.feature_list:
count_forward = i
count_backward = i
while count_forward < all_words and sentence.tagged[
count_forward][1] != 'JJ':
count_forward += 1
while count_backward >= 0 and sentence.tagged[
count_backward][1] != 'JJ':
count_backward -= 1
count = -1
if count_forward < all_words and count_backward >= 0:
if (count_forward - i > i - count_backward):
count = count_backward
else:
count = count_forward
if count_backward < 0:
count = count_forward
if count_forward >= all_words:
count = count_backward
if count_forward >= all_words and count_backward < 0:
count = -1
if count != -1:
feature_count[j[0]].append(
sentence.tagged[count][0])
# Make the feature class that contain the feature count of positive opinion and negative opinion
for i in feature_count.keys():
self.feature_result.append(
Feature(self.db, i, feature_count[i], self.id_anime))
# Use wordnet to mine the opinion orientation
Feature.OrientationPrediction(Feature.all_adj, Feature.seed)
# print(Feature.seed)
def get_summary(self):
self.extract_feature_list()
self.extract_feature_opinion()
res = {}
for i in self.feature_result:
i.save()
res[i.feature] = (i.good, i.bad)
return res
# Save method
def save(self):
for feature in self.feature_result:
feature.save()
# Close database
def close_db(self):
self.db.close()
def index():
db = Database(db_name)
row = db.read('anime_list', ['id', 'judul'])
return render_template('index.html', data_title=row)