def frequency_naver_news(self, filePath, encoding):
df = handler.load_to_csv(filePath, encoding)
contents = df['content'].tolist()
print('list load end')
total = []
okt = Okt()
for content in contents:
content = str(content)
content = content.replace("\n","")
# print(f'content : {content}')
noun = okt.nouns(content)
for i,v in enumerate(noun):
if len(v)<2:
noun.pop(i)
total.extend(noun)
count = Counter(total)
noun_list = count.most_common(100)
print('noun_list load end')
for value in noun_list:
print(value)
colnames = ['word', 'count']
return pd.DataFrame(noun_list, columns=colnames)
def get_df_news_word(self, filePath, encoding):
df = handler.load_to_csv(filePath, encoding)
contents = df['contents'].tolist()
# times = df['time'].tolist()
# print('list load end')
total = []
stopWords = [
'크게', '여기', '서울', '정부', '위원회', '사업', '한국', '옵티머스', '의원', '금융감독원',
'국회', '지난'
]
okt = Okt()
for content in contents:
content = str(content)
# print(f'content : {content}')
# noun = okt.nouns(content)
morph = okt.pos(content)
for word, tag in morph:
if tag in ['Noun'] and len(word) > 1 and word not in stopWords:
total.append(word)
count = Counter(total)
noun_list = count.most_common(10)
# print('noun_list load end')
# for value in noun_list:
# print(value)
# print('create wordclude')
# wc = WordCloud(font_path='./font/NanumBarunGothic.ttf', background_color="white", width=1000, height=1000, max_words=50, max_font_size=300)
# wc.generate_from_frequencies(dict(noun_list))
# wc.to_file('wordCloud.png')
colnames = ['word', 'count']
if not Checker.check_folder_path('./csv'):
handler.crete_folder('./csv')
handler.save_to_csv('./csv/result_economy_news_word.csv', noun_list,
colnames, 'utf-8-sig')
return pd.DataFrame(noun_list, columns=colnames)
def get_df_news_word(self, filePath, encoding):
df = handler.load_to_csv(filePath, encoding)
contents = df['contents'].tolist()
# times = df['time'].tolist()
print('list load end')
total = []
stopWords = ['지난', '진자', '판정', '대통령', '위해', '지역', '사람', '관련', '이후', '대해', '개발', '올해', '당국',
'경우', '국내', '때문', '조사', '최근', '이번', '확인', '증가', '진행', '통해', '신종', '지난달', '대상'
'단계', '우리', '상황', '현재', '조치']
okt = Okt()
for content in contents:
content = str(content)
# print(f'content : {content}')
# noun = okt.nouns(content)
morph = okt.pos(content)
for word, tag in morph:
if tag in ['Noun'] and len(word) > 1 and word not in stopWords:
total.append(word)
count = Counter(total)
noun_list = count.most_common(30)
# print('noun_list load end')
if not Checker.check_folder_path('./csv'):
handler.crete_folder('./csv')
handler.save_to_csv('./csv/result_covid_news_word.csv', noun_list, ['word','count'], 'utf-8-sig')
# for value in noun_list:
# print(value)
# print('create wordclude')
# wc = WordCloud(font_path='./font/NanumBarunGothic.ttf', background_color="white", width=1000, height=1000, max_words=50, max_font_size=300)
# wc.generate_from_frequencies(dict(noun_list))
# wc.to_file('wordCloud.png')
colnames = ['word', 'count']
return pd.DataFrame(noun_list, columns=colnames)
def predict_hana_date(self, csv_path, date, repeat_count):
data = FileHandler.load_to_csv(csv_path, 'utf-8 sig')
pred_values, real_values, dates = self.predict_data_with_time(
self.hana_model, data, date, repeat_count)
return pred_values, real_values, dates
def predict_hana(self, csv_path):
datas = FileHandler.load_to_csv(csv_path, 'utf-8 sig')
return self.predict_data(self.hana_model, datas)
def predict_celltrion(self, csv_path):
datas = FileHandler.load_to_csv(csv_path, 'utf-8 sig')
return self.predict_data(self.celltrion_model, datas)