2021-1-capstone-design1 / HCG_Project1

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1 +import warnings
2 +#########5/23일작성중 #########
3 +warnings.simplefilter(("ignore"))
4 +import openpyxl
5 +import pandas as pd
6 +
7 +####### [날짜, 뉴스단어 한개] 구성 만드는 파일 차트 분석 시 count함수 이욜 할 때 참조 자료
8 +# ex)hmm뉴스키워드날짜뉴스모으고특수삭제.xlsx 파일 넣음<- DayNewsMerge.py 중간에 주석처리 된 부분 해제하고 결과 얻기
9 +Stockfilename = input("키워드파일이름입력:")
10 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/샘플/" + Stockfilename + ".xlsx"
11 +Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
12 +stock_ws = Stockfile.active
13 +Stock_data = [] # list 타입
14 +date=[]
15 +i = 0
16 +for row in stock_ws.rows:
17 + Stock_data.append([])
18 + date.append(row[1].value)
19 + for cell in row:
20 + if cell.value != None :
21 + Stock_data[i].append(cell.value)
22 + i += 1
23 +del Stock_data[0]
24 +del date[0]
25 +for i in range(len(Stock_data)):
26 + del Stock_data[i][0] #각 열의 첫번째 행 삭제
27 +for i in range(len(Stock_data)):
28 + del Stock_data[i][0] #각 열의 첫번째 행 삭제
29 +print(Stock_data)
30 +print(date)
31 +a=[] #
32 +print(len(date),len(Stock_data))
33 +for j in range(len(Stock_data)):
34 + for k in range(len(Stock_data[j])):
35 + a.append([date[j],Stock_data[j][k]])
36 +print(a)
37 +df_SourTar = pd.DataFrame(a)
38 +df_SourTar.to_excel(Stockfilename+'countif.xlsx',sheet_name='sheet1')
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1 +import pandas as pd
2 +
3 +source = {
4 + '학년': [1, 2, 1, 3, 4],
5 + '성별': ['남자', '여자', '남자', '여자', '남자'],
6 + '국어': [98, 88, 92, 63, 120],
7 + '영어': [88, 90, 70, 60, 50],
8 + '수학': [64, 62, None, 31, None],
9 + '과학': [None, 72, None, 70, 88]
10 +}
11 +df = pd.DataFrame(source)
12 +df1=df[(df['성별']!='남자')].index
13 +df2=df.drop(df1)
14 +print(df)
15 +print("---------")
16 +print(df2)
1 +import warnings
2 +
3 +warnings.simplefilter(("ignore"))
4 +import openpyxl
5 +import pandas as pd
6 +
7 +# 000_KNU_New_Vdic2.xlsx 파일 넣기
8 +
9 +Stockfilefolder = input("종목시세폴더입력: ")
10 +Stockfilename = input("시세파일이름입력:")
11 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
12 +Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
13 +stock_ws = Stockfile.active
14 +Stock_data = [] # list 타입
15 +i = 0
16 +for row in stock_ws.rows:
17 + Stock_data.append([])
18 + for cell in row:
19 + if cell.value != None:
20 + Stock_data[i].append(cell.value)
21 + i += 1
22 +del Stock_data[0]
23 +for i in range(len(Stock_data)):
24 + del Stock_data[i][0]
25 +#print(Stock_data)
26 +
27 +
28 +vert_p = [] # 수직 중복 삭제
29 +for i in range(len(Stock_data)):
30 + vert_p.append([])
31 + for j in range(len(Stock_data[i])):
32 + vert_p[i].append(Stock_data[i][j]) # 단어만 넣기
33 +print(vert_p)
34 +
35 +vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
36 +for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적, 다르면 값 바꾸기
37 + for j in range(i + 1, len(vert_p)):
38 + if vert_p[i][0] == vert_p[j][0] :
39 + vert_p[i][1] += vert_p[j][1]
40 + vert_p[j] = ['0', 0]
41 + if str.isalnum(vert_p[i][0]) == False:
42 + vert_p[i] =['0', 0]
43 +
44 +vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
45 +df_ver = pd.DataFrame(vert_p)
46 +df_ver.to_excel(Stockfilename + ' Stock_dictionary2.xlsx', sheet_name='sheet1')
47 +####사전 완성####
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1 +import warnings
2 +
3 +warnings.simplefilter(("ignore"))
4 +import openpyxl
5 +import pandas as pd
6 +
7 +Stockfilefolder = input("종목시세폴더입력: ")
8 +Stockfilename = input("시세파일이름입력:")
9 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
10 +Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
11 +stock_ws = Stockfile.active
12 +Stock_data = [] # list 타입
13 +i = 0
14 +for row in stock_ws.rows:
15 + Stock_data.append([])
16 + for cell in row:
17 + if cell.value != None:
18 + Stock_data[i].append(cell.value)
19 + i += 1
20 +del Stock_data[0]
21 +for i in range(len(Stock_data)):
22 + del Stock_data[i][0] # 대비 삭제
23 +#print(Stock_data)
24 +
25 +
26 +vert_p = [] # 수직 중복 삭제
27 +for i in range(len(Stock_data)):
28 + vert_p.append([])
29 + for j in range(len(Stock_data[i])):
30 + vert_p[i].append(Stock_data[i][j]) # 단어만 넣기
31 +print(vert_p)
32 +
33 +vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
34 +for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적 다르면 값 바꾸기
35 + for j in range(i + 1, len(vert_p)):
36 + if vert_p[i][0] == vert_p[j][0] :
37 + vert_p[i][1] += vert_p[j][1]
38 + vert_p[j] = ['0', 0]
39 + if str.isalnum(vert_p[i][0]) == False:
40 + vert_p[i] =['0', 0]
41 +
42 +vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
43 +df_ver = pd.DataFrame(vert_p)
44 +df_ver.to_excel(Stockfilename + ' Stock_dictionary2.xlsx', sheet_name='sheet1')
1 +# **뉴스 키워드 노출 빈도수에 따른 기업 주가 영향 분석**
2 +---------------------------------------------------
3 +----------------------------------------------------
4 +
5 +
6 +## **지도교수님**
7 +* 한치근 교수님
8 +
9 +## **팀원**
10 +* 2017104003 컴퓨터공학과 양지수
11 +* 2017104039 컴퓨터공학과 한서흔
12 +
13 +---
14 +
15 +## **개발일정**
16 +* 2021.03.09~2021.03.22 데이터 수집
17 +* 2021.03.23~2021.04.19 자연어 처리,중간보고서 작성
18 +* 2021.04.20~2021.05.04 감성사전 분석 및 특화 사전 제작
19 +* 2021.05.05~ 중심성 분석 및 최종 결과 제작
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1 +import warnings
2 +warnings.simplefilter(("ignore"))
3 +import konlpy
4 +from konlpy.tag import *
5 +import openpyxl
6 +import pandas as pd
7 +from math import log10
8 +import numpy as np
9 +
10 +#형태소분석라이브러리
11 +#okt = Okt()
12 +hannanum = Hannanum()
13 +#filename= input("분석할 파일이름 입력:") #파일명
14 +filefolder = input("종목폴더입력: ")
15 +filename=input("파일이름입력:")
16 +filepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스크롤링/"+filefolder+"/" + filename + ".xlsx"
17 +kfile = openpyxl.load_workbook(filepos)#파일이름입력
18 +sheet=kfile.worksheets[0]#sheet1에 있는 데이터 가죠오기
19 +#print(sheet)
20 +data=[]
21 +for row in sheet.rows: #data에 크롤링한 뉴스 제목들 저장
22 + data.append(
23 + row[1].value
24 + )
25 +#print(data)
26 +#print(type(data[1])) #str
27 +
28 +newData2=[]
29 +
30 +#print(newData)
31 +for i in range(len(data)-1):
32 + newData2.append(hannanum.nouns(data[i+1])) #명사만 추출hannanum가 okt보다 성능좋음
33 +#print(newData2)
34 +
35 +newData3=[]
36 +for i in range(len(newData2)):
37 + newData3.append([])
38 + for j in newData2[i]:
39 + if any(map(str.isdigit,j))==False and len(j)>1: #추출한 결과가 숫자포함이거나 한글자 인것 제외
40 + newData3[i].append(j)
41 +#print(newData3)
42 +
43 +#print(type(newData2))#newData2 데이터 형식은 list
44 +#df= pd.DataFrame.from_records(newData3)#newData3 dataframe으로 변환
45 +#df.to_excel(filename+'_명사추출_숫자제외'+'.xlsx') #파일명의 엑셀로 변환
46 +
47 +#TF-IDF함수 시작
48 +
49 +def f(t, d): # 엑셀 d 안에 있는 t 빈도 세기
50 + return d.count(t)
51 +
52 +def tf(t, d): #tf(t,d)증가빈도 공식 적용
53 + return 0.5 + 0.5*f(t,d)/max([f(w,d) for w in d])
54 +
55 +def idf(t, D): #역문서 빈도 공식 적용
56 + numerator = len(D) #문서 집합에 포함 된 문서 수
57 + denominator = 1 + len([ True for d in D if t in d]) #1더해서 0되는 것 방지
58 + return log10(numerator/denominator)
59 +
60 +def tfidf(t, d, D):
61 + return tf(t,d)*idf(t, D)
62 +
63 +def tfidfScorer(D):
64 + result = []
65 + for d in D:
66 + result.append([(t, tfidf(t, d, D)) for t in d] )
67 + return result
68 +
69 +#newData3는 명사추출을 통해 분리되어있음(이미 split상태)
70 +
71 +if __name__ == '__main__':
72 + corpus=[]
73 + for i in range(len(newData3)):
74 + corpus.append(newData3[i])
75 + TfIf=[] #결과저장
76 + for i, result in enumerate(tfidfScorer(corpus)):
77 + #print('====== document[%d] ======' % i)
78 + #print(result)
79 + TfIf.append(result)
80 +print(TfIf)#TFIF는 (단어,가중치) 조합으로 저장
81 +for i in range(len(TfIf)-1):
82 + TfIf[i].sort(key=lambda x:x[1], reverse=True) #가중치기준 내림차순 정렬
83 +print(TfIf)
84 +onlynouns = []
85 +for i in range(len(TfIf)):
86 + onlynouns.append([])
87 + for j in range(len(TfIf[i])):
88 + for k in range(len(TfIf[i][j])):
89 + if k%2==0:
90 + onlynouns[i].append(TfIf[i][j][k])
91 +print(onlynouns)
92 +#df= pd.DataFrame.from_records(TfIf)#TfIf dataframe으로 변환
93 +#df.to_excel(filename+'_가중치추출_내림정렬'+'.xlsx')
94 +
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431 + <option name="LAST_RESOLUTION" value="IGNORE" />
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433 + <component name="Git.Settings">
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436 + <component name="ProjectId" id="1sKE1vqX6hUM7q0Ohx8nPpqQpTo" />
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452 + <property name="node.js.selected.package.tslint" value="(autodetect)" />
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455 + <configuration name="knusl" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
456 + <module name="knu" />
457 + <option name="INTERPRETER_OPTIONS" value="" />
458 + <option name="PARENT_ENVS" value="true" />
459 + <envs>
460 + <env name="PYTHONUNBUFFERED" value="1" />
461 + </envs>
462 + <option name="SDK_HOME" value="" />
463 + <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/KnuSentiLex" />
464 + <option name="IS_MODULE_SDK" value="true" />
465 + <option name="ADD_CONTENT_ROOTS" value="true" />
466 + <option name="ADD_SOURCE_ROOTS" value="true" />
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500 + </component>
501 +</project>
...\ No newline at end of file ...\ No newline at end of file
1 +import warnings
2 +
3 +warnings.simplefilter(("ignore"))
4 +import openpyxl
5 +import pandas as pd
6 +
7 +#######Gephi에 사용할 edge파일 만들기 전에 필요한 자료 만드는 과정
8 +####6개월치 키워드 합친 키워드 파일 넣기
9 +
10 +Stockfilename = input("키워드파일이름입력:")
11 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/샘플/" + Stockfilename + ".xlsx"
12 +Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
13 +stock_ws = Stockfile.active
14 +Stock_data = [] # list 타입
15 +date=[]
16 +i = 0
17 +for row in stock_ws.rows:
18 + Stock_data.append([])
19 + date.append(row[1].value)
20 + for cell in row:
21 + if cell.value != None:
22 + Stock_data[i].append(cell.value)
23 + i += 1
24 +del Stock_data[0] #첫번째 의미없는 열 삭제
25 +del date[0]
26 +for i in range(len(Stock_data)):
27 + del Stock_data[i][0] #각 열의 첫번째 행 삭제
28 +for i in range(len(Stock_data)):
29 + del Stock_data[i][0] #각 열의 날짜 행 삭제
30 +
31 +
32 +Tdata=[]
33 +
34 +for x in range(len(Stock_data)):
35 + Tdata.append([])
36 + for y in range(len(Stock_data[x])):
37 + if str.isalnum(Stock_data[x][y]) == True:
38 + Tdata[x].append(Stock_data[x][y])
39 +
40 +result = { '날짜':date, '단어':Tdata }
41 +
42 +df = pd.DataFrame(result)
43 +#print(df)
44 +list_df=df.values.tolist() #dataframe list로 변경
45 +print(list_df)
46 +#print(list_df[0][0]) 날짜 2021.01.01.
47 +
48 +new_date = [] # 날짜 중복 삭제
49 +for v in date:
50 + if v not in new_date:
51 + new_date.append(v)
52 +#print(new_date)
53 +
54 +Setlist =[]# 날짜별 키워드 넣기
55 +for v in range(len(new_date)):
56 + Setlist.append([])
57 + Setlist[v].append(new_date[v])
58 + for i in range(len(list_df)):
59 + for j in range(len(list_df[i][1])):
60 + if new_date[v] == list_df[i][0] :
61 + Setlist[v].append(list_df[i][1][j])
62 +print(Setlist)
63 +df_ver= pd.DataFrame(Setlist)
64 +#df_ver.to_excel(Stockfilename+' 날짜뉴스모으고특수삭제.xlsx',sheet_name='sheet1')
65 +SourceTarget=[]
66 +for i in range(len(list_df)):
67 + SourceTarget.append([])
68 + for j in range(len(list_df[i][1])-1):
69 + SourceTarget.append([list_df[i][0],list_df[i][1][j],list_df[i][1][j+1],1])
70 +print(SourceTarget)
71 +SourceTarget = [v for v in SourceTarget if v]
72 +df_SourTar = pd.DataFrame(SourceTarget)
73 +df_SourTar.to_excel(Stockfilename+'Edge3.xlsx',sheet_name='sheet1')
...\ No newline at end of file ...\ No newline at end of file
1 +# KNU 한국어 감성사전
2 +# 작성자 : 온병원, 박상민, 나철원
3 +# 소속 : 군산대학교 소프트웨어융합공학과 Data Intelligence Lab
4 +# 홈페이지 : dilab.kunsan.ac.kr
5 +# 작성일 : 2018.05.14
6 +# 뜻풀이 데이터 출처 : https://github.com/mrchypark/stdkor
7 +# 신조어 데이터 출처 : https://ko.wikipedia.org/wiki/%EB%8C%80%ED%95%9C%EB%AF%BC%EA%B5%AD%EC%9D%98_%EC%9D%B8%ED%84%B0%EB%84%B7_%EC%8B%A0%EC%A1%B0%EC%96%B4_%EB%AA%A9%EB%A1%9D
8 +# 이모티콘 데이터 출처: https://ko.wikipedia.org/wiki/%EC%9D%B4%EB%AA%A8%ED%8B%B0%EC%BD%98
9 +# SentiWordNet_3.0.0_20130122 데이터 출처 : http://sentiwordnet.isti.cnr.it/
10 +# SenticNet-5.0 데이터 출처 : http://sentic.net/
11 +# 감정단어사전0603 데이터 출처 : http://datascience.khu.ac.kr/board/bbs/board.php?bo_table=05_01&wr_id=91
12 +# 김은영, “국어 감정동사 연구”, 2004.02, 학위논문(박사) - 전남대학교 국어국문학과 대학원
13 +
14 +#-*-coding:utf-8-*-
15 +import collections
16 +import json
17 +
18 +import warnings
19 +warnings.simplefilter(("ignore"))
20 +import openpyxl
21 +import pandas as pd
22 +import re
23 +from datetime import datetime
24 +
25 +############종목 감성 판단 ex)hmm뉴스키워드.xlsx 파일 넣는 과정
26 +class KnuSL():
27 +
28 + def data_list(wordname):
29 + with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
30 + data = json.load(f)
31 + result = [0,0]
32 +
33 + for i in range(0, len(data)):
34 + if data[i]['word'] == wordname:
35 + result.pop()
36 + result.pop()
37 + result.append(data[i]['word_root'])
38 + result.append(int(data[i]['polarity']))
39 +
40 + r_word = result[0] #어근
41 + s_word = result[1] #극성
42 +
43 + return s_word
44 +
45 +if __name__ == "__main__":
46 +
47 + ksl = KnuSL
48 +
49 + print("\nKNU 한국어 감성사전입니다~ :)")
50 + print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
51 + print("종료하시려면 #을 입력해주세요!!!")
52 + print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
53 + print("\n")
54 +#########
55 +Newsfilefolder = input("종목폴더입력: ")
56 +Newsfilename=input("파일이름입력:")
57 +Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/"+Newsfilefolder+"/" + Newsfilename + ".xlsx"
58 +Newsfile = openpyxl.load_workbook(Newsfilepos)#파일이름입력
59 +ws=Newsfile.active
60 +data=[]
61 +date=[]
62 +i=0
63 +for row in ws.rows:
64 + data.append([])
65 + date.append(row[1].value)
66 + for cell in row:
67 + if cell.value != None:
68 + data[i].append(cell.value)
69 + i += 1
70 +del data[0] #첫번째 의미없는 열 삭제
71 +del date[0]
72 +for i in range(len(data)):
73 + del data[i][0] #각 열의 첫번째 행 삭제
74 +for i in range(len(data)):
75 + del data[i][0] #각 열의 날짜 행 삭제
76 +
77 +KNUdata=[]
78 +Tdata=[]
79 +
80 +for x in range(len(data)):
81 + KNUdata.append([])
82 + Tdata.append([])
83 + for y in range(len(data[x])):
84 + KNUdata[x].append(ksl.data_list(data[x][y]))
85 + Tdata[x].append([data[x][y], KNUdata[x][y]])
86 +
87 +result = { '날짜':date, '단어, 극성':Tdata }
88 +
89 +df = pd.DataFrame(result)
90 +#print(df)
91 +list_df=df.values.tolist() #dataframe list로 변경
92 +#print(list_df)
93 +#print(list_df[0][0]) 날짜 2021.01.01.
94 +
95 +new_date = [] # 날짜 중복 삭제
96 +for v in date:
97 + if v not in new_date:
98 + new_date.append(v)
99 +#print(new_date)
100 +
101 +Setlist =[]# 날짜별 키워드 넣기
102 +for v in range(len(new_date)):
103 + Setlist.append([])
104 + Setlist[v].append(new_date[v])
105 + for i in range(len(list_df)):
106 + for j in range(len(list_df[i][1])):
107 + if new_date[v] == list_df[i][0]:
108 + Setlist[v].append(list_df[i][1][j])
109 +print(Setlist)
110 +print(Setlist[0][0]) #2021.01.01
111 +print(type(Setlist[0][0]))
112 +print(Setlist[0][0].split('-'))
113 +print(Setlist[0][1][1]) #극성 0
114 +print(type(Setlist[0][1][1])) #극성 모든 타입 int
115 +
116 +#print(list_df[0][1][0]) 키워드와 극성 ['HMM…"체질개선해', 'X']
117 +#print(list_df[0][1][0][1]) 극성 x
118 +#print(list_df[0][0].split('.')[:3]) ['2021', '01', '01']
119 +#df.to_excel(Newsfilename+' KNU.xlsx',sheet_name='sheet1')
120 +
121 +Stockfilefolder = input("종목시세폴더입력: ")
122 +Stockfilename=input("시세파일이름입력:")
123 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/"+Stockfilefolder+"/" + Stockfilename + ".xlsx"
124 +Stockfile = openpyxl.load_workbook(fileStock)#파일이름입력
125 +stock_ws=Stockfile.active
126 +Stock_data=[] #list 타입
127 +i=0
128 +for row in stock_ws.rows:
129 + Stock_data.append([])
130 + for cell in row:
131 + if cell.value != None:
132 + Stock_data[i].append(cell.value)
133 + i += 1
134 +del Stock_data[0]
135 +for i in range(len(Stock_data)):
136 + del Stock_data[i][2] # 대비 삭제
137 +for i in range(len(Stock_data)):
138 + del Stock_data[i][7] #거래대금 삭제
139 +for i in range(len(Stock_data)):
140 + del Stock_data[i][7] #시가 총액 삭제
141 +for i in range(len(Stock_data)):
142 + del Stock_data[i][7] #상장주식 수 삭제 / 결과:'일자', '종가', '등락률', '시가', '고가', '저가', '거래량'
143 +#print(Stock_data)
144 +
145 +def Calpercentage(a,b): #시초가 대비 고점/저점 비율
146 + return abs(a-b)/a*100
147 +####아래로 수정 필요 (미완성)####
148 +
149 +i=0
150 +for k in range(len(Setlist)):
151 + if( Stock_data[i][0].split('/') == Setlist[k][0].split('-')): # 날짜 비교 날짜가 같다면
152 + if Calpercentage(Stock_data[i][3],Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
153 + for j in range(1,len(Setlist[k])):
154 + if Setlist[k][j][1] == 0:
155 + Setlist[k][j][1] = 1
156 + else:
157 + Setlist[0][j][1] += 1
158 + elif Calpercentage(Stock_data[i][3],Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
159 + for j in range(1,len(Setlist[k])):
160 + if Setlist[k][j][1] == 0:
161 + Setlist[k][j][1] = -1
162 + else:
163 + Setlist[0][j][1] -= 1
164 + else:
165 + if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
166 + for j in range(1,len(Setlist[k])): #어제뉴스는 호재 취급
167 + if Setlist[k][j][1] == 0:
168 + Setlist[k][j][1] = 1
169 + else:
170 + Setlist[k][j][1] += 1
171 + elif Stock_data[i+1][2] < 0:
172 + for j in range(1,len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
173 + if Setlist[k][j][1] == 0:
174 + Setlist[k][j][1] = -1
175 + else:
176 + Setlist[k][j][1] -= 1
177 + i+=1
178 + else:
179 + if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2: # 당일 시가 대비 고가가 2퍼 높을때
180 + for j in range(1, len(Setlist[k])):
181 + if Setlist[k][j][1] == 0:
182 + Setlist[k][j][1] = 1
183 + else:
184 + Setlist[k][j][1] += 1
185 + elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2: # 당일 시가 대비 저가가 2퍼 낮을 때
186 + for j in range(1, len(Setlist[k])):
187 + if Setlist[k][j][1] == 0:
188 + Setlist[k][j][1] = -1
189 + else:
190 + Setlist[k][j][1] -= 1
191 + else:
192 + if Stock_data[i + 1][2] > 0: # 다음날 주가 등락률이 양수면
193 + for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
194 + if Setlist[k][j][1] == 0:
195 + Setlist[k][j][1] = 1
196 + else:
197 + Setlist[k][j][1] += 1
198 + elif Stock_data[i + 1][2] < 0:
199 + for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
200 + if Setlist[k][j][1] == 0:
201 + Setlist[k][j][1] = -1
202 + else:
203 + Setlist[k][j][1] -= 1
204 + i+=1 #<이거 삭제서 hmm한번 더 돌려보기
205 +
206 +print(Setlist)
207 +
208 +#df_Setlist = pd.DataFrame(Setlist)
209 +#df_Setlist.to_excel(Stockfilename+' KNU_New.xlsx',sheet_name='sheet1')
210 +
211 +Setlist_w = []
212 +for i in range(len(Setlist)):
213 + Setlist_w.append([])
214 + for j in range(1, len(Setlist[i])):
215 + Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
216 +
217 +counter = {}
218 +for i in range(len(Setlist_w)):
219 + counter[i] = collections.Counter(Setlist_w[i]) # 누적치
220 +
221 +for i in range(len(Setlist_w)):
222 + Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
223 +
224 +Plist = []
225 +for i in range(len(Setlist_w)):
226 + Plist.append([])
227 + for j in range(len(Setlist_w[i])):
228 + Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
229 +
230 +for i in range(len(Plist)):
231 + for j in range(len(Plist[i])):
232 + Plist[i][j][1] = 0 # 극성 0으로 초기화
233 +
234 +for i in range(len(Setlist)):
235 + for j in range(1, len(Setlist[i])):
236 + for h in range(len(Plist[i])):
237 + if Setlist[i][j][0] == Plist[i][h][0]:
238 + Plist[i][h][1] += Setlist[i][j][1] #누적치
239 +vert_p=[] #수직 중복 삭제
240 +for i in range(len(Plist)):
241 + for j in range(len(Plist[i])):
242 + vert_p.append(Plist[i][j]) #단어만 넣기
243 +#print(vert_p)
244 +vert_p.sort(key=lambda x:x[0]) #단어 기준으로 정렬
245 +for i in range(len(vert_p)-2): #단어 비교해서 같으면 누적 다르면 값 바꾸기
246 + for j in range(i+1,len(vert_p)):
247 + if vert_p[i][0] == vert_p[j][0]:
248 + vert_p[i][1]+=vert_p[j][1]
249 + vert_p[j]=['0',0]
250 +print(vert_p)
251 +vert_p=[i for i in vert_p if not '0' in i] #'0'들어간 열 제거
252 +df_ver= pd.DataFrame(vert_p)
253 +df_ver.to_excel(Stockfilename+' KNU_New_Vdic2.xlsx',sheet_name='sheet1')
254 +
255 +####키워드파일 월별로 돌려서 그 나온 결과 파일들을 합쳐서 Merge_dictionay.py에 넣어서 사전 만들기 ####
256 +
257 +
1 +# KNU 한국어 감성사전
2 +# 작성자 : 온병원, 박상민, 나철원
3 +# 소속 : 군산대학교 소프트웨어융합공학과 Data Intelligence Lab
4 +# 홈페이지 : dilab.kunsan.ac.kr
5 +# 작성일 : 2018.05.14
6 +# 뜻풀이 데이터 출처 : https://github.com/mrchypark/stdkor
7 +# 신조어 데이터 출처 : https://ko.wikipedia.org/wiki/%EB%8C%80%ED%95%9C%EB%AF%BC%EA%B5%AD%EC%9D%98_%EC%9D%B8%ED%84%B0%EB%84%B7_%EC%8B%A0%EC%A1%B0%EC%96%B4_%EB%AA%A9%EB%A1%9D
8 +# 이모티콘 데이터 출처: https://ko.wikipedia.org/wiki/%EC%9D%B4%EB%AA%A8%ED%8B%B0%EC%BD%98
9 +# SentiWordNet_3.0.0_20130122 데이터 출처 : http://sentiwordnet.isti.cnr.it/
10 +# SenticNet-5.0 데이터 출처 : http://sentic.net/
11 +# 감정단어사전0603 데이터 출처 : http://datascience.khu.ac.kr/board/bbs/board.php?bo_table=05_01&wr_id=91
12 +# 김은영, “국어 감정동사 연구”, 2004.02, 학위논문(박사) - 전남대학교 국어국문학과 대학원
13 +
14 +#-*-coding:utf-8-*-
15 +import collections
16 +import json
17 +
18 +import warnings
19 +warnings.simplefilter(("ignore"))
20 +import openpyxl
21 +import pandas as pd
22 +import re
23 +from datetime import datetime
24 +
25 +class KnuSL():
26 +
27 + def data_list(wordname):
28 + with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
29 + data = json.load(f)
30 + result = [0,0]
31 +
32 + for i in range(0, len(data)):
33 + if data[i]['word'] == wordname:
34 + result.pop()
35 + result.pop()
36 + result.append(data[i]['word_root'])
37 + result.append(int(data[i]['polarity']))
38 +
39 + r_word = result[0] #어근
40 + s_word = result[1] #극성
41 +
42 + return s_word
43 +
44 +if __name__ == "__main__":
45 +
46 + ksl = KnuSL
47 +
48 + print("\nKNU 한국어 감성사전입니다~ :)")
49 + print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
50 + print("종료하시려면 #을 입력해주세요!!!")
51 + print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
52 + print("\n")
53 +#########
54 +Newsfilefolder = input("종목폴더입력: ")
55 +Newsfilename=input("파일이름입력:")
56 +Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/"+Newsfilefolder+"/" + Newsfilename + ".xlsx"
57 +Newsfile = openpyxl.load_workbook(Newsfilepos)#파일이름입력
58 +ws=Newsfile.active
59 +data=[]
60 +date=[]
61 +i=0
62 +for row in ws.rows:
63 + data.append([])
64 + date.append(row[1].value)
65 + for cell in row:
66 + if cell.value != None:
67 + data[i].append(cell.value)
68 + i += 1
69 +del data[0] #첫번째 의미없는 열 삭제
70 +del date[0]
71 +for i in range(len(data)):
72 + del data[i][0] #각 열의 첫번째 행 삭제
73 +for i in range(len(data)):
74 + del data[i][0] #각 열의 날짜 행 삭제
75 +
76 +KNUdata=[]
77 +Tdata=[]
78 +
79 +for x in range(len(data)):
80 + KNUdata.append([])
81 + Tdata.append([])
82 + for y in range(len(data[x])):
83 + KNUdata[x].append(ksl.data_list(data[x][y]))
84 + Tdata[x].append([data[x][y], KNUdata[x][y]])
85 +
86 +result = { '날짜':date, '단어, 극성':Tdata }
87 +
88 +df = pd.DataFrame(result)
89 +#print(df)
90 +list_df=df.values.tolist() #dataframe list로 변경
91 +#print(list_df)
92 +#print(list_df[0][0]) 날짜 2021.01.01.
93 +
94 +new_date = [] # 날짜 중복 삭제
95 +for v in date:
96 + if v not in new_date:
97 + new_date.append(v)
98 +#print(new_date)
99 +
100 +Setlist =[]# 날짜별 키워드 넣기
101 +for v in range(len(new_date)):
102 + Setlist.append([])
103 + Setlist[v].append(new_date[v])
104 + for i in range(len(list_df)):
105 + for j in range(len(list_df[i][1])):
106 + if new_date[v] == list_df[i][0]:
107 + Setlist[v].append(list_df[i][1][j])
108 +#print(Setlist)
109 +#print(Setlist[0][0]) #2021.01.01
110 +#print(Setlist[0][1][1]) #극성 0
111 +#print(type(Setlist[0][1][1])) #극성 모든 타입 int
112 +
113 +#print(list_df[0][1][0]) 키워드와 극성 ['HMM…"체질개선해', 'X']
114 +#print(list_df[0][1][0][1]) 극성 x
115 +#print(list_df[0][0].split('.')[:3]) ['2021', '01', '01']
116 +#df.to_excel(Newsfilename+' KNU.xlsx',sheet_name='sheet1')
117 +
118 +Stockfilefolder = input("종목시세폴더입력: ")
119 +Stockfilename=input("시세파일이름입력:")
120 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/"+Stockfilefolder+"/" + Stockfilename + ".xlsx"
121 +Stockfile = openpyxl.load_workbook(fileStock)#파일이름입력
122 +stock_ws=Stockfile.active
123 +Stock_data=[] #list 타입
124 +i=0
125 +for row in stock_ws.rows:
126 + Stock_data.append([])
127 + for cell in row:
128 + if cell.value != None:
129 + Stock_data[i].append(cell.value)
130 + i += 1
131 +del Stock_data[0]
132 +for i in range(len(Stock_data)):
133 + del Stock_data[i][2] # 대비 삭제
134 +for i in range(len(Stock_data)):
135 + del Stock_data[i][7] #거래대금 삭제
136 +for i in range(len(Stock_data)):
137 + del Stock_data[i][7] #시가 총액 삭제
138 +for i in range(len(Stock_data)):
139 + del Stock_data[i][7] #상장주식 수 삭제 / 결과:'일자', '종가', '등락률', '시가', '고가', '저가', '거래량'
140 +#print(Stock_data)
141 +
142 +def Calpercentage(a,b): #시초가 대비 고점/저점 비율
143 + return abs(a-b)/a*100
144 +####아래로 수정 필요 (미완성)####
145 +
146 +i=0
147 +for k in range(len(Setlist)):
148 + if( Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교 날짜가 같다면
149 + if Calpercentage(Stock_data[i][3],Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
150 + for j in range(1,len(Setlist[k])):
151 + if Setlist[k][j][1] == 0:
152 + Setlist[k][j][1] = 1
153 + else:
154 + Setlist[0][j][1] += 1
155 + elif Calpercentage(Stock_data[i][3],Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
156 + for j in range(1,len(Setlist[k])):
157 + if Setlist[k][j][1] == 0:
158 + Setlist[k][j][1] = -1
159 + else:
160 + Setlist[0][j][1] -= 1
161 + else:
162 + if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
163 + for j in range(1,len(Setlist[k])): #어제뉴스는 호재 취급
164 + if Setlist[k][j][1] == 0:
165 + Setlist[k][j][1] = 1
166 + else:
167 + Setlist[k][j][1] += 1
168 + elif Stock_data[i+1][2] < 0:
169 + for j in range(1,len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
170 + if Setlist[k][j][1] == 0:
171 + Setlist[k][j][1] = -1
172 + else:
173 + Setlist[k][j][1] -= 1
174 + i+=1
175 + else:
176 + if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2: # 당일 시가 대비 고가가 2퍼 높을때
177 + for j in range(1, len(Setlist[k])):
178 + if Setlist[k][j][1] == 0:
179 + Setlist[k][j][1] = 1
180 + else:
181 + Setlist[k][j][1] += 1
182 + elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2: # 당일 시가 대비 저가가 2퍼 낮을 때
183 + for j in range(1, len(Setlist[k])):
184 + if Setlist[k][j][1] == 0:
185 + Setlist[k][j][1] = -1
186 + else:
187 + Setlist[k][j][1] -= 1
188 + else:
189 + if Stock_data[i + 1][2] > 0: # 다음날 주가 등락률이 양수면
190 + for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
191 + if Setlist[k][j][1] == 0:
192 + Setlist[k][j][1] = 1
193 + else:
194 + Setlist[k][j][1] += 1
195 + elif Stock_data[i + 1][2] < 0:
196 + for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
197 + if Setlist[k][j][1] == 0:
198 + Setlist[k][j][1] = -1
199 + else:
200 + Setlist[k][j][1] -= 1
201 + i+=1
202 +
203 +print(Setlist)
204 +'''
205 +Stock_dic=[]
206 +
207 +for i in range(len(Setlist)):
208 + Stock_dic.append([])
209 + j = 2
210 + for k in range(1,len(Setlist[i])-1):
211 + if Setlist[i][k][0]== Setlist[i][j][0]:
212 + Setlist[i][k][1]+=Setlist[i][j][1]
213 + Stock_dic.append(Setlist[i][k])
214 + j+=1
215 + else:
216 + Stock_dic.append(Setlist[i][k])
217 + j+=1
218 +print(Stock_dic)
219 +'''
220 +
221 +#df_Setlist = pd.DataFrame(Setlist)
222 +#df_Setlist.to_excel(Stockfilename+' KNU_New.xlsx',sheet_name='sheet1')
223 +
224 +Setlist_w = []
225 +for i in range(len(Setlist)):
226 + Setlist_w.append([])
227 + for j in range(1, len(Setlist[i])):
228 + Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
229 +
230 +counter = {}
231 +for i in range(len(Setlist_w)):
232 + counter[i] = collections.Counter(Setlist_w[i]) # 누적치
233 +
234 +for i in range(len(Setlist_w)):
235 + Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
236 +
237 +Plist = []
238 +for i in range(len(Setlist_w)):
239 + Plist.append([])
240 + for j in range(len(Setlist_w[i])):
241 + Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
242 +
243 +for i in range(len(Plist)):
244 + for j in range(len(Plist[i])):
245 + Plist[i][j][1] = 0 # 극성 0으로 초기화
246 +
247 +for i in range(len(Setlist)):
248 + for j in range(1, len(Setlist[i])):
249 + for h in range(len(Plist[i])):
250 + if Setlist[i][j][0] == Plist[i][h][0]:
251 + Plist[i][h][1] += Setlist[i][j][1] #누적치
252 +vert_p=[] #수직 중복 삭제
253 +for i in range(len(Plist)):
254 + for j in range(len(Plist[i])):
255 + vert_p.append(Plist[i][j]) #단어만 넣기
256 +#print(vert_p)
257 +vert_p.sort(key=lambda x:x[0]) #단어 기준으로 정렬
258 +for i in range(len(vert_p)-2): #단어 비교해서 같으면 누적 다르면 값 바꾸기
259 + for j in range(i+1,len(vert_p)):
260 + if vert_p[i][0] == vert_p[j][0]:
261 + vert_p[i][1]+=vert_p[j][1]
262 + vert_p[j]=['0',0]
263 +print(vert_p)
264 +vert_p=[i for i in vert_p if not '0' in i] #'0'들어간 열 제거
265 +df_ver= pd.DataFrame(vert_p)
266 +df_ver.to_excel(Stockfilename+' KNU_New_vdic2.xlsx',sheet_name='sheet1')
267 +
268 +#p_result = {'날짜': new_date, '단어, 극성': Plist}
269 +#print(p_result)
270 +#df_p_result = pd.DataFrame(p_result)
271 +#df_p_result.to_excel(Stockfilename+' KNU_New_dic.xlsx',sheet_name='sheet1')
272 +'''
273 +for i in range(len(Stock_data)-1):
274 + for k in range(len(Setlist)):
275 + if (Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교
276 + if Calpercentage(Stock_data[i][3], Stock_data[i][4]) > 2 : #당일 시가 대비 고가가 2퍼 높을때
277 + for j in range(1, len(Setlist[k])):
278 + if Setlist[k][j][1] == 0:
279 + Setlist[k][j][1] = 1
280 + else:
281 + Setlist[k][j][1] += 1
282 + elif Calpercentage(Stock_data[i][3], Stock_data[i][5]) < -2 : #당일 시가 대비 저가가 2퍼 낮을 때
283 + for j in range(1, len(Setlist[k])):
284 + if Setlist[k][j][1] == 0:
285 + Setlist[k][j][1] = -1
286 + else:
287 + Setlist[k][j][1] -= 1
288 + else:
289 + if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
290 + for j in range(1, len(Setlist[k])): #어제뉴스는 호재 취급
291 + if Calpercentage(Stock_data[i+1][3], Stock_data[i+1][5]) < -2 :
292 + for j in range(1, len(Setlist[k])):
293 + if Setlist[k][j][1] == 0:
294 + Setlist[k][j][1] = -1
295 + else:
296 + Setlist[k][j][1] -= 1
297 + else:
298 + if Setlist[k][j][1] == 0:
299 + Setlist[k][j][1] = 1
300 + else:
301 + Setlist[k][j][1] += 1
302 + elif Stock_data[i+1][2] < 0:
303 + if Calpercentage(Stock_data[i+1][3], Stock_data[i+1][4]) > 2 :
304 + for j in range(1, len(Setlist[k])):
305 + if Setlist[k][j][1] == 0:
306 + Setlist[k][j][1] = 1
307 + else:
308 + Setlist[k][j][1] += 1
309 + else:
310 + for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
311 + if Setlist[k][j][1] == 0:
312 + Setlist[k][j][1] = -1
313 + else:
314 + Setlist[k][j][1] -= -1
315 + else:
316 +
317 +
318 +print(Setlist)
319 +
320 +'''
1 +# -*-coding:utf-8-*-
2 +import collections
3 +import json
4 +
5 +import warnings
6 +
7 +warnings.simplefilter(("ignore"))
8 +import openpyxl
9 +import pandas as pd
10 +import re
11 +from datetime import datetime
12 +
13 +########코스피 감성 판단
14 +class KnuSL():
15 +
16 + def data_list(wordname):
17 + with open('KnuSentiLex/data/SentiWord_info.json', encoding='utf-8-sig', mode='r') as f:
18 + data = json.load(f)
19 + result = [0, 0]
20 +
21 + for i in range(0, len(data)):
22 + if data[i]['word'] == wordname:
23 + result.pop()
24 + result.pop()
25 + result.append(data[i]['word_root'])
26 + result.append(int(data[i]['polarity']))
27 +
28 + r_word = result[0] # 어근
29 + s_word = result[1] # 극성
30 +
31 + return s_word
32 +
33 +
34 +if __name__ == "__main__":
35 + ksl = KnuSL
36 +
37 + print("\nKNU 한국어 감성사전입니다~ :)")
38 + print("사전에 단어가 없는 경우 결과가 None으로 나타납니다!!!")
39 + print("종료하시려면 #을 입력해주세요!!!")
40 + print("-2:매우 부정, -1:부정, 0:중립 or Unkwon, 1:긍정, 2:매우 긍정")
41 + print("\n")
42 +#########
43 +Newsfilefolder = input("종목폴더입력: ")
44 +Newsfilename = input("파일이름입력:")
45 +Newsfilepos = "C:/Users/yangj/PycharmProjects/pythonProject1/뉴스키워드/" + Newsfilefolder + "/" + Newsfilename + ".xlsx"
46 +Newsfile = openpyxl.load_workbook(Newsfilepos) # 파일이름입력
47 +ws = Newsfile.active
48 +data = []
49 +date = []
50 +i = 0
51 +for row in ws.rows:
52 + data.append([])
53 + date.append(row[1].value)
54 + for cell in row:
55 + if cell.value != None:
56 + data[i].append(cell.value)
57 + i += 1
58 +del data[0] # 첫번째 의미없는 열 삭제
59 +del date[0]
60 +for i in range(len(data)):
61 + del data[i][0] # 각 열의 첫번째 행 삭제
62 +for i in range(len(data)):
63 + del data[i][0] # 각 열의 날짜 행 삭제
64 +
65 +KNUdata = []
66 +Tdata = []
67 +
68 +for x in range(len(data)):
69 + KNUdata.append([])
70 + Tdata.append([])
71 + for y in range(len(data[x])):
72 + KNUdata[x].append(ksl.data_list(data[x][y]))
73 + Tdata[x].append([data[x][y], KNUdata[x][y]])
74 +
75 +result = {'날짜': date, '단어, 극성': Tdata}
76 +
77 +df = pd.DataFrame(result)
78 +
79 +list_df = df.values.tolist() # dataframe list로 변경
80 +new_date = [] # 날짜 중복 삭제
81 +for v in date:
82 + if v not in new_date:
83 + new_date.append(v)
84 +# print(new_date)
85 +
86 +Setlist = [] # 날짜별 키워드 넣기
87 +for v in range(len(new_date)):
88 + Setlist.append([])
89 + Setlist[v].append(new_date[v])
90 + for i in range(len(list_df)):
91 + for j in range(len(list_df[i][1])):
92 + if new_date[v] == list_df[i][0]:
93 + Setlist[v].append(list_df[i][1][j])
94 +
95 +Stockfilefolder = input("종목시세폴더입력: ")
96 +Stockfilename = input("시세파일이름입력:")
97 +fileStock = "C:/Users/yangj/PycharmProjects/pythonProject1/종목별시세/" + Stockfilefolder + "/" + Stockfilename + ".xlsx"
98 +Stockfile = openpyxl.load_workbook(fileStock) # 파일이름입력
99 +stock_ws = Stockfile.active
100 +Stock_data = [] # list 타입
101 +i = 0
102 +for row in stock_ws.rows:
103 + Stock_data.append([])
104 + for cell in row:
105 + if cell.value != None:
106 + Stock_data[i].append(cell.value)
107 + i += 1
108 +del Stock_data[0]
109 +for i in range(len(Stock_data)):
110 + del Stock_data[i][2] # 대비 삭제
111 +for i in range(len(Stock_data)):
112 + del Stock_data[i][7] # 거래대금 삭제
113 +for i in range(len(Stock_data)):
114 + del Stock_data[i][7] # 시가 총액 삭제
115 +
116 +
117 +i = 0
118 +for k in range(len(Setlist)):
119 + if (Stock_data[i][0].split('/') == Setlist[k][0].split('.')[:3]): # 날짜 비교 날짜가 같다면
120 + if Stock_data[i][2] > 0: # 코스피 등락이 양수
121 + for j in range(1, len(Setlist[k])):
122 + if Setlist[k][j][1] == 0:
123 + Setlist[k][j][1] = 1
124 + else:
125 + Setlist[k][j][1] += 1
126 + elif Stock_data[i][2] < 0:
127 + for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
128 + if Setlist[k][j][1] == 0:
129 + Setlist[k][j][1] = -1
130 + else:
131 + Setlist[k][j][1] -= 1
132 + i += 1
133 + else:
134 + if Stock_data[i+1][2] > 0: # 다음날 주가 등락률이 양수면
135 + for j in range(1, len(Setlist[k])): # 어제뉴스는 호재 취급
136 + if Setlist[k][j][1] == 0:
137 + Setlist[k][j][1] = 1
138 + else:
139 + Setlist[k][j][1] += 1
140 + elif Stock_data[i+1][2] < 0:
141 + for j in range(1, len(Setlist[k])): # 음수면 어제 뉴스는 악재 취급
142 + if Setlist[k][j][1] == 0:
143 + Setlist[k][j][1] = -1
144 + else:
145 + Setlist[k][j][1] -= 1
146 +
147 +Setlist_w = []
148 +for i in range(len(Setlist)):
149 + Setlist_w.append([])
150 + for j in range(1, len(Setlist[i])):
151 + Setlist_w[i].append(Setlist[i][j][0]) # 극성 제외 단어만 추출
152 +
153 +counter = {}
154 +for i in range(len(Setlist_w)):
155 + counter[i] = collections.Counter(Setlist_w[i]) # 누적치
156 +
157 +for i in range(len(Setlist_w)):
158 + Setlist_w[i] = list(zip(counter[i].keys(), counter[i].values())) # 튜플 리스트화 [(값, 값)]
159 +
160 +Plist = []
161 +for i in range(len(Setlist_w)):
162 + Plist.append([])
163 + for j in range(len(Setlist_w[i])):
164 + Plist[i].append(list(Setlist_w[i][j])) # 튜플 -> 리스트화 [[값, 값]]
165 +
166 +for i in range(len(Plist)):
167 + for j in range(len(Plist[i])):
168 + Plist[i][j][1] = 0 # 극성 0으로 초기화
169 +
170 +for i in range(len(Setlist)):
171 + for j in range(1, len(Setlist[i])):
172 + for h in range(len(Plist[i])):
173 + if Setlist[i][j][0] == Plist[i][h][0]:
174 + Plist[i][h][1] += Setlist[i][j][1] # 누적치
175 +vert_p = [] # 수직 중복 삭제
176 +for i in range(len(Plist)):
177 + for j in range(len(Plist[i])):
178 + vert_p.append(Plist[i][j]) # 단어만 넣기
179 +# print(vert_p)
180 +vert_p.sort(key=lambda x: x[0]) # 단어 기준으로 정렬
181 +for i in range(len(vert_p) - 2): # 단어 비교해서 같으면 누적 다르면 값 바꾸기
182 + for j in range(i + 1, len(vert_p)):
183 + if vert_p[i][0] == vert_p[j][0]:
184 + vert_p[i][1] += vert_p[j][1]
185 + vert_p[j] = ['0', 0]
186 +
187 +vert_p = [i for i in vert_p if not '0' in i] # '0'들어간 열 제거
188 +df_ver = pd.DataFrame(vert_p)
189 +df_ver.to_excel(Stockfilename + ' KNU_New_vdic2.xlsx', sheet_name='sheet1')
1 +# This file was automatically generated by SWIG (http://www.swig.org).
2 +# Version 3.0.0
3 +#
4 +# Do not make changes to this file unless you know what you are doing--modify
5 +# the SWIG interface file instead.
6 +
7 +
8 +
9 +
10 +
11 +from sys import version_info
12 +if version_info >= (2,6,0):
13 + def swig_import_helper():
14 + from os.path import dirname
15 + import imp
16 + fp = None
17 + try:
18 + fp, pathname, description = imp.find_module('_MeCab', [dirname(__file__)])
19 + except ImportError:
20 + import _MeCab
21 + return _MeCab
22 + if fp is not None:
23 + try:
24 + _mod = imp.load_module('_MeCab', fp, pathname, description)
25 + finally:
26 + fp.close()
27 + return _mod
28 + _MeCab = swig_import_helper()
29 + del swig_import_helper
30 +else:
31 + import _MeCab
32 +del version_info
33 +try:
34 + _swig_property = property
35 +except NameError:
36 + pass # Python < 2.2 doesn't have 'property'.
37 +def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
38 + if (name == "thisown"): return self.this.own(value)
39 + if (name == "this"):
40 + if type(value).__name__ == 'SwigPyObject':
41 + self.__dict__[name] = value
42 + return
43 + method = class_type.__swig_setmethods__.get(name,None)
44 + if method: return method(self,value)
45 + if (not static):
46 + self.__dict__[name] = value
47 + else:
48 + raise AttributeError("You cannot add attributes to %s" % self)
49 +
50 +def _swig_setattr(self,class_type,name,value):
51 + return _swig_setattr_nondynamic(self,class_type,name,value,0)
52 +
53 +def _swig_getattr(self,class_type,name):
54 + if (name == "thisown"): return self.this.own()
55 + method = class_type.__swig_getmethods__.get(name,None)
56 + if method: return method(self)
57 + raise AttributeError(name)
58 +
59 +def _swig_repr(self):
60 + try: strthis = "proxy of " + self.this.__repr__()
61 + except: strthis = ""
62 + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,)
63 +
64 +try:
65 + _object = object
66 + _newclass = 1
67 +except AttributeError:
68 + class _object : pass
69 + _newclass = 0
70 +
71 +
72 +class DictionaryInfo(_object):
73 + __swig_setmethods__ = {}
74 + __setattr__ = lambda self, name, value: _swig_setattr(self, DictionaryInfo, name, value)
75 + __swig_getmethods__ = {}
76 + __getattr__ = lambda self, name: _swig_getattr(self, DictionaryInfo, name)
77 + __repr__ = _swig_repr
78 + __swig_getmethods__["filename"] = _MeCab.DictionaryInfo_filename_get
79 + if _newclass:filename = _swig_property(_MeCab.DictionaryInfo_filename_get)
80 + __swig_getmethods__["charset"] = _MeCab.DictionaryInfo_charset_get
81 + if _newclass:charset = _swig_property(_MeCab.DictionaryInfo_charset_get)
82 + __swig_getmethods__["size"] = _MeCab.DictionaryInfo_size_get
83 + if _newclass:size = _swig_property(_MeCab.DictionaryInfo_size_get)
84 + __swig_getmethods__["type"] = _MeCab.DictionaryInfo_type_get
85 + if _newclass:type = _swig_property(_MeCab.DictionaryInfo_type_get)
86 + __swig_getmethods__["lsize"] = _MeCab.DictionaryInfo_lsize_get
87 + if _newclass:lsize = _swig_property(_MeCab.DictionaryInfo_lsize_get)
88 + __swig_getmethods__["rsize"] = _MeCab.DictionaryInfo_rsize_get
89 + if _newclass:rsize = _swig_property(_MeCab.DictionaryInfo_rsize_get)
90 + __swig_getmethods__["version"] = _MeCab.DictionaryInfo_version_get
91 + if _newclass:version = _swig_property(_MeCab.DictionaryInfo_version_get)
92 + __swig_getmethods__["next"] = _MeCab.DictionaryInfo_next_get
93 + if _newclass:next = _swig_property(_MeCab.DictionaryInfo_next_get)
94 + def __init__(self):
95 + this = _MeCab.new_DictionaryInfo()
96 + try: self.this.append(this)
97 + except: self.this = this
98 + __swig_destroy__ = _MeCab.delete_DictionaryInfo
99 + __del__ = lambda self : None;
100 +DictionaryInfo_swigregister = _MeCab.DictionaryInfo_swigregister
101 +DictionaryInfo_swigregister(DictionaryInfo)
102 +
103 +class Path(_object):
104 + __swig_setmethods__ = {}
105 + __setattr__ = lambda self, name, value: _swig_setattr(self, Path, name, value)
106 + __swig_getmethods__ = {}
107 + __getattr__ = lambda self, name: _swig_getattr(self, Path, name)
108 + def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
109 + __repr__ = _swig_repr
110 + __swig_getmethods__["rnode"] = _MeCab.Path_rnode_get
111 + if _newclass:rnode = _swig_property(_MeCab.Path_rnode_get)
112 + __swig_getmethods__["rnext"] = _MeCab.Path_rnext_get
113 + if _newclass:rnext = _swig_property(_MeCab.Path_rnext_get)
114 + __swig_getmethods__["lnode"] = _MeCab.Path_lnode_get
115 + if _newclass:lnode = _swig_property(_MeCab.Path_lnode_get)
116 + __swig_getmethods__["lnext"] = _MeCab.Path_lnext_get
117 + if _newclass:lnext = _swig_property(_MeCab.Path_lnext_get)
118 + __swig_getmethods__["cost"] = _MeCab.Path_cost_get
119 + if _newclass:cost = _swig_property(_MeCab.Path_cost_get)
120 + __swig_setmethods__["prob"] = _MeCab.Path_prob_set
121 + __swig_getmethods__["prob"] = _MeCab.Path_prob_get
122 + if _newclass:prob = _swig_property(_MeCab.Path_prob_get, _MeCab.Path_prob_set)
123 +Path_swigregister = _MeCab.Path_swigregister
124 +Path_swigregister(Path)
125 +
126 +class Node(_object):
127 + __swig_setmethods__ = {}
128 + __setattr__ = lambda self, name, value: _swig_setattr(self, Node, name, value)
129 + __swig_getmethods__ = {}
130 + __getattr__ = lambda self, name: _swig_getattr(self, Node, name)
131 + def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined")
132 + __repr__ = _swig_repr
133 + __swig_getmethods__["prev"] = _MeCab.Node_prev_get
134 + if _newclass:prev = _swig_property(_MeCab.Node_prev_get)
135 + __swig_getmethods__["next"] = _MeCab.Node_next_get
136 + if _newclass:next = _swig_property(_MeCab.Node_next_get)
137 + __swig_getmethods__["enext"] = _MeCab.Node_enext_get
138 + if _newclass:enext = _swig_property(_MeCab.Node_enext_get)
139 + __swig_getmethods__["bnext"] = _MeCab.Node_bnext_get
140 + if _newclass:bnext = _swig_property(_MeCab.Node_bnext_get)
141 + __swig_getmethods__["rpath"] = _MeCab.Node_rpath_get
142 + if _newclass:rpath = _swig_property(_MeCab.Node_rpath_get)
143 + __swig_getmethods__["lpath"] = _MeCab.Node_lpath_get
144 + if _newclass:lpath = _swig_property(_MeCab.Node_lpath_get)
145 + __swig_getmethods__["feature"] = _MeCab.Node_feature_get
146 + if _newclass:feature = _swig_property(_MeCab.Node_feature_get)
147 + __swig_getmethods__["id"] = _MeCab.Node_id_get
148 + if _newclass:id = _swig_property(_MeCab.Node_id_get)
149 + __swig_getmethods__["length"] = _MeCab.Node_length_get
150 + if _newclass:length = _swig_property(_MeCab.Node_length_get)
151 + __swig_getmethods__["rlength"] = _MeCab.Node_rlength_get
152 + if _newclass:rlength = _swig_property(_MeCab.Node_rlength_get)
153 + __swig_getmethods__["rcAttr"] = _MeCab.Node_rcAttr_get
154 + if _newclass:rcAttr = _swig_property(_MeCab.Node_rcAttr_get)
155 + __swig_getmethods__["lcAttr"] = _MeCab.Node_lcAttr_get
156 + if _newclass:lcAttr = _swig_property(_MeCab.Node_lcAttr_get)
157 + __swig_getmethods__["posid"] = _MeCab.Node_posid_get
158 + if _newclass:posid = _swig_property(_MeCab.Node_posid_get)
159 + __swig_getmethods__["char_type"] = _MeCab.Node_char_type_get
160 + if _newclass:char_type = _swig_property(_MeCab.Node_char_type_get)
161 + __swig_getmethods__["stat"] = _MeCab.Node_stat_get
162 + if _newclass:stat = _swig_property(_MeCab.Node_stat_get)
163 + __swig_getmethods__["isbest"] = _MeCab.Node_isbest_get
164 + if _newclass:isbest = _swig_property(_MeCab.Node_isbest_get)
165 + __swig_getmethods__["alpha"] = _MeCab.Node_alpha_get
166 + if _newclass:alpha = _swig_property(_MeCab.Node_alpha_get)
167 + __swig_getmethods__["beta"] = _MeCab.Node_beta_get
168 + if _newclass:beta = _swig_property(_MeCab.Node_beta_get)
169 + __swig_setmethods__["prob"] = _MeCab.Node_prob_set
170 + __swig_getmethods__["prob"] = _MeCab.Node_prob_get
171 + if _newclass:prob = _swig_property(_MeCab.Node_prob_get, _MeCab.Node_prob_set)
172 + __swig_getmethods__["wcost"] = _MeCab.Node_wcost_get
173 + if _newclass:wcost = _swig_property(_MeCab.Node_wcost_get)
174 + __swig_getmethods__["cost"] = _MeCab.Node_cost_get
175 + if _newclass:cost = _swig_property(_MeCab.Node_cost_get)
176 + __swig_getmethods__["surface"] = _MeCab.Node_surface_get
177 + if _newclass:surface = _swig_property(_MeCab.Node_surface_get)
178 +Node_swigregister = _MeCab.Node_swigregister
179 +Node_swigregister(Node)
180 +
181 +MECAB_NOR_NODE = _MeCab.MECAB_NOR_NODE
182 +MECAB_UNK_NODE = _MeCab.MECAB_UNK_NODE
183 +MECAB_BOS_NODE = _MeCab.MECAB_BOS_NODE
184 +MECAB_EOS_NODE = _MeCab.MECAB_EOS_NODE
185 +MECAB_EON_NODE = _MeCab.MECAB_EON_NODE
186 +MECAB_SYS_DIC = _MeCab.MECAB_SYS_DIC
187 +MECAB_USR_DIC = _MeCab.MECAB_USR_DIC
188 +MECAB_UNK_DIC = _MeCab.MECAB_UNK_DIC
189 +MECAB_ONE_BEST = _MeCab.MECAB_ONE_BEST
190 +MECAB_NBEST = _MeCab.MECAB_NBEST
191 +MECAB_PARTIAL = _MeCab.MECAB_PARTIAL
192 +MECAB_MARGINAL_PROB = _MeCab.MECAB_MARGINAL_PROB
193 +MECAB_ALTERNATIVE = _MeCab.MECAB_ALTERNATIVE
194 +MECAB_ALL_MORPHS = _MeCab.MECAB_ALL_MORPHS
195 +MECAB_ALLOCATE_SENTENCE = _MeCab.MECAB_ALLOCATE_SENTENCE
196 +MECAB_ANY_BOUNDARY = _MeCab.MECAB_ANY_BOUNDARY
197 +MECAB_TOKEN_BOUNDARY = _MeCab.MECAB_TOKEN_BOUNDARY
198 +MECAB_INSIDE_TOKEN = _MeCab.MECAB_INSIDE_TOKEN
199 +class Lattice(_object):
200 + __swig_setmethods__ = {}
201 + __setattr__ = lambda self, name, value: _swig_setattr(self, Lattice, name, value)
202 + __swig_getmethods__ = {}
203 + __getattr__ = lambda self, name: _swig_getattr(self, Lattice, name)
204 + __repr__ = _swig_repr
205 + def clear(self): return _MeCab.Lattice_clear(self)
206 + def is_available(self): return _MeCab.Lattice_is_available(self)
207 + def bos_node(self): return _MeCab.Lattice_bos_node(self)
208 + def eos_node(self): return _MeCab.Lattice_eos_node(self)
209 + def end_nodes(self, *args): return _MeCab.Lattice_end_nodes(self, *args)
210 + def begin_nodes(self, *args): return _MeCab.Lattice_begin_nodes(self, *args)
211 + def sentence(self): return _MeCab.Lattice_sentence(self)
212 + def size(self): return _MeCab.Lattice_size(self)
213 + def set_Z(self, *args): return _MeCab.Lattice_set_Z(self, *args)
214 + def Z(self): return _MeCab.Lattice_Z(self)
215 + def set_theta(self, *args): return _MeCab.Lattice_set_theta(self, *args)
216 + def theta(self): return _MeCab.Lattice_theta(self)
217 + def next(self): return _MeCab.Lattice_next(self)
218 + def request_type(self): return _MeCab.Lattice_request_type(self)
219 + def has_request_type(self, *args): return _MeCab.Lattice_has_request_type(self, *args)
220 + def set_request_type(self, *args): return _MeCab.Lattice_set_request_type(self, *args)
221 + def add_request_type(self, *args): return _MeCab.Lattice_add_request_type(self, *args)
222 + def remove_request_type(self, *args): return _MeCab.Lattice_remove_request_type(self, *args)
223 + def newNode(self): return _MeCab.Lattice_newNode(self)
224 + def toString(self, *args): return _MeCab.Lattice_toString(self, *args)
225 + def enumNBestAsString(self, *args): return _MeCab.Lattice_enumNBestAsString(self, *args)
226 + def has_constraint(self): return _MeCab.Lattice_has_constraint(self)
227 + def boundary_constraint(self, *args): return _MeCab.Lattice_boundary_constraint(self, *args)
228 + def feature_constraint(self, *args): return _MeCab.Lattice_feature_constraint(self, *args)
229 + def set_boundary_constraint(self, *args): return _MeCab.Lattice_set_boundary_constraint(self, *args)
230 + def set_feature_constraint(self, *args): return _MeCab.Lattice_set_feature_constraint(self, *args)
231 + def set_result(self, *args): return _MeCab.Lattice_set_result(self, *args)
232 + def what(self): return _MeCab.Lattice_what(self)
233 + def set_what(self, *args): return _MeCab.Lattice_set_what(self, *args)
234 + __swig_destroy__ = _MeCab.delete_Lattice
235 + __del__ = lambda self : None;
236 + def __init__(self):
237 + this = _MeCab.new_Lattice()
238 + try: self.this.append(this)
239 + except: self.this = this
240 + def set_sentence(self, *args): return _MeCab.Lattice_set_sentence(self, *args)
241 +Lattice_swigregister = _MeCab.Lattice_swigregister
242 +Lattice_swigregister(Lattice)
243 +
244 +class Model(_object):
245 + __swig_setmethods__ = {}
246 + __setattr__ = lambda self, name, value: _swig_setattr(self, Model, name, value)
247 + __swig_getmethods__ = {}
248 + __getattr__ = lambda self, name: _swig_getattr(self, Model, name)
249 + __repr__ = _swig_repr
250 + def dictionary_info(self): return _MeCab.Model_dictionary_info(self)
251 + def transition_cost(self, *args): return _MeCab.Model_transition_cost(self, *args)
252 + def lookup(self, *args): return _MeCab.Model_lookup(self, *args)
253 + def createTagger(self): return _MeCab.Model_createTagger(self)
254 + def createLattice(self): return _MeCab.Model_createLattice(self)
255 + def swap(self, *args): return _MeCab.Model_swap(self, *args)
256 + __swig_getmethods__["version"] = lambda x: _MeCab.Model_version
257 + if _newclass:version = staticmethod(_MeCab.Model_version)
258 + __swig_destroy__ = _MeCab.delete_Model
259 + __del__ = lambda self : None;
260 + __swig_getmethods__["create"] = lambda x: _MeCab.Model_create
261 + if _newclass:create = staticmethod(_MeCab.Model_create)
262 + def __init__(self, *args):
263 + this = _MeCab.new_Model(*args)
264 + try: self.this.append(this)
265 + except: self.this = this
266 +Model_swigregister = _MeCab.Model_swigregister
267 +Model_swigregister(Model)
268 +
269 +def Model_version():
270 + return _MeCab.Model_version()
271 +Model_version = _MeCab.Model_version
272 +
273 +def Model_create(*args):
274 + return _MeCab.Model_create(*args)
275 +Model_create = _MeCab.Model_create
276 +
277 +class Tagger(_object):
278 + __swig_setmethods__ = {}
279 + __setattr__ = lambda self, name, value: _swig_setattr(self, Tagger, name, value)
280 + __swig_getmethods__ = {}
281 + __getattr__ = lambda self, name: _swig_getattr(self, Tagger, name)
282 + __repr__ = _swig_repr
283 + def parse(self, *args): return _MeCab.Tagger_parse(self, *args)
284 + def parseToNode(self, *args): return _MeCab.Tagger_parseToNode(self, *args)
285 + def parseNBest(self, *args): return _MeCab.Tagger_parseNBest(self, *args)
286 + def parseNBestInit(self, *args): return _MeCab.Tagger_parseNBestInit(self, *args)
287 + def nextNode(self): return _MeCab.Tagger_nextNode(self)
288 + def next(self): return _MeCab.Tagger_next(self)
289 + def formatNode(self, *args): return _MeCab.Tagger_formatNode(self, *args)
290 + def set_request_type(self, *args): return _MeCab.Tagger_set_request_type(self, *args)
291 + def request_type(self): return _MeCab.Tagger_request_type(self)
292 + def partial(self): return _MeCab.Tagger_partial(self)
293 + def set_partial(self, *args): return _MeCab.Tagger_set_partial(self, *args)
294 + def lattice_level(self): return _MeCab.Tagger_lattice_level(self)
295 + def set_lattice_level(self, *args): return _MeCab.Tagger_set_lattice_level(self, *args)
296 + def all_morphs(self): return _MeCab.Tagger_all_morphs(self)
297 + def set_all_morphs(self, *args): return _MeCab.Tagger_set_all_morphs(self, *args)
298 + def set_theta(self, *args): return _MeCab.Tagger_set_theta(self, *args)
299 + def theta(self): return _MeCab.Tagger_theta(self)
300 + def dictionary_info(self): return _MeCab.Tagger_dictionary_info(self)
301 + def what(self): return _MeCab.Tagger_what(self)
302 + __swig_destroy__ = _MeCab.delete_Tagger
303 + __del__ = lambda self : None;
304 + __swig_getmethods__["create"] = lambda x: _MeCab.Tagger_create
305 + if _newclass:create = staticmethod(_MeCab.Tagger_create)
306 + __swig_getmethods__["version"] = lambda x: _MeCab.Tagger_version
307 + if _newclass:version = staticmethod(_MeCab.Tagger_version)
308 + def __init__(self, *args):
309 + this = _MeCab.new_Tagger(*args)
310 + try: self.this.append(this)
311 + except: self.this = this
312 + def parseToString(self, *args): return _MeCab.Tagger_parseToString(self, *args)
313 +Tagger_swigregister = _MeCab.Tagger_swigregister
314 +Tagger_swigregister(Tagger)
315 +
316 +def Tagger_create(*args):
317 + return _MeCab.Tagger_create(*args)
318 +Tagger_create = _MeCab.Tagger_create
319 +
320 +def Tagger_version():
321 + return _MeCab.Tagger_version()
322 +Tagger_version = _MeCab.Tagger_version
323 +
324 +VERSION = _MeCab.VERSION
325 +# This file is compatible with both classic and new-style classes.
326 +
327 +
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1 +Metadata-Version: 2.1
2 +Name: mecab-python
3 +Version: 0.996-ko-0.9.2-msvc
4 +Summary: UNKNOWN
5 +Home-page: UNKNOWN
6 +Author: UNKNOWN
7 +Author-email: UNKNOWN
8 +License: UNKNOWN
9 +Platform: UNKNOWN
10 +
11 +UNKNOWN
12 +
13 +
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3 +__pycache__/MeCab.cpython-37.pyc,,
4 +libmecab.dll,sha256=lRhwboyrXWEXXnDn8soNA05id3UvWfPpP7bvTXj5iH4,1908736
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7 +mecab_python-0.996_ko_0.9.2_msvc.dist-info/RECORD,,
8 +mecab_python-0.996_ko_0.9.2_msvc.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
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11 +mecab_python-0.996_ko_0.9.2_msvc.dist-info/top_level.txt,sha256=E6HHbCcV114TjQmzLJGG5aSu2Sb0tjGGxkjler1jxrQ,13
1 +Wheel-Version: 1.0
2 +Generator: bdist_wheel (0.31.1)
3 +Root-Is-Purelib: false
4 +Tag: cp37-cp37m-win_amd64
5 +
1 +{"archive_info": {}, "url": "file:///C:/Users/yangj/PycharmProjects/pythonProject1/mecab_python-0.996_ko_0.9.2_msvc-cp37-cp37m-win_amd64.whl"}
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