In [4]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import requests
import urllib.request
import json
import requests
import re
from bs4 import BeautifulSoup
In [17]:
%matplotlib inline
In [5]:
url = 'https://play.google.com/store/apps/details?id=com.venticake.retrica&hl=ko#details-reviews'
headers = {'User-Agent': 'Mozilla/5.0 (Windows; U; MSIE 9.0; Windows NT 9.0; en-US)'}
req = urllib.request.Request(url, headers=headers)
data = urllib.request.urlopen(req).read().decode('utf-8')
In [6]:
bs = BeautifulSoup(data, 'html.parser')
div_list = bs.find_all('div',class_="details-section-body expandable")
div_root_list = bs.find_all('div',class_="details-wrapper apps")
- 각 리뷰 스코오별 투표 인원 가지고 오기
- 1~5점 각 인원, 평균, 전체 인원
In [7]:
# 각 리뷰 스코오별 투표 인원 가지고 오기
# 1~5점 각 인원, 평균, 전체 인원
for div_list in div_root_list:
rating = div_list.find_all('div',class_="rating-box")
if len(rating)>0:
total_num = rating[0].find_all('span', class_="reviews-num")[0].text
avg = rating[0].find_all("div",class_="score")[0].text
score_detail = rating[0].find_all("div",class_="rating-histogram")[0].find_all('div',class_="rating-bar-container")
total_number = {}
for score in score_detail:
label = score.find('span',class_="bar-label").text.strip()
label_number = score.find('span',class_="bar-number").text
total_number[label] = label_number
total_number['avg'] = avg
total_number['total'] = total_num
In [8]:
total_series = pd.Series(total_number)
total_series.to_csv("total_series.csv")
In [9]:
total_series
Out[9]:
In [10]:
url = "https://play.google.com/store/getreviews?authuser=0"
id = 'com.venticake.retrica'
token = 'ZLqR3TmB64y6koyq8uj1tqqiQ4k:14191636750027'
In [11]:
# Review가지고오기.
def GetReviews(url,id,token,pages):
param = {'reviewType': '0', 'pageNum': '10000', 'id':'','reviewSortOrder':'4','xhr':'1','token':'','hl':'ko'}
param['id'] = id
param['token'] = token
review_date_all = []
review_star_all = []
review_user_all = []
review_title_all = []
review_body_all = []
for i in range(1,pages):
param['pageNum'] = i
res = requests.post(url, data=param)
print(" line : {line}, code : {code}".format(line=i,code=res.status_code))
if res.status_code == 400 or i == pages-1:
review_df = pd.DataFrame({'DATE':review_date_all,'STAR':review_star_all,'TITLE':review_title_all, 'USER':review_user_all,'BODY':review_body_all})
columns_list = ['DATE','USER','STAR','TITLE','BODY']
return(review_df[columns_list])
else:
body = res.text[6:]
res_json = json.loads(body)
bs = BeautifulSoup(res_json[0][2], 'html.parser')
review_lists = bs.find_all('div',class_="single-review")
for j in range(1,len(review_lists)):
review_date = review_lists[j].find('span',class_="review-date").text # review_date
review_star = int(re.findall(r'\d+', review_lists[0].find('div',class_="tiny-star star-rating-non-editable-container")['aria-label'])[1]) # Get Digits from Rates
review_title = review_lists[j].find('span',class_="review-title").text # title
review_body = review_lists[j].find('div',class_="review-body with-review-wrapper").text # review body
review_body = str.replace(review_body,review_title,"")
review_body = str.replace(review_body,"전체 리뷰","")
review_body = review_body.strip()
review_user = review_lists[j].find('span',class_="author-name").text.strip() # get Reviewer
review_date_all.append(review_date)
review_star_all.append(review_star)
review_user_all.append(review_user)
review_title_all.append(review_title)
review_body_all.append(review_body)
In [12]:
DF = GetReviews(url,id,token,10000)
In [13]:
DF['DATE'] = pd.to_datetime(DF['DATE'],format='%Y년 %m월 %d일')
DF = DF.sort_values(by='DATE', ascending=True).reindex()
In [14]:
DF.head()
Out[14]:
In [15]:
DF.to_csv("Retrica_review.csv")
In [18]:
sns.factorplot('STAR',kind='count',data=DF) # 실질적으로 1을 준 사용자도 많다. 완전히 실망하거나 만족하거나 하는 성향을 보였다.
Out[18]:
In [19]:
Under_three = DF[DF['STAR']<=3]
Over_three = DF[DF['STAR']>3]
In [20]:
len(Under_three) + len(Over_three)
Out[20]:
In [21]:
len(DF)
Out[21]:
In [22]:
under_text = Under_three['BODY']
In [27]:
under_text[2499]
Out[27]:
In [29]:
re.sub('[^가-힣\s]',"",under_text[2499])
Out[29]:
In [30]:
under_text = under_text.apply(lambda x:re.sub('[^가-힣\s]',"",x))
In [31]:
from konlpy.tag import Twitter
from sklearn.feature_extraction.text import CountVectorizer
In [34]:
tagger = Twitter()
In [35]:
def get_word(doc):
nouns = tagger.nouns(doc)
return [noun for noun in nouns if len(noun) > 1]
In [36]:
cv = CountVectorizer(tokenizer=get_word, max_features=200)
tdf = cv.fit_transform(under_text)
words = cv.get_feature_names()
In [38]:
words[:10]
Out[38]:
In [39]:
import numpy as np
count_mat = tdf.sum(axis=0)
count_mat
Out[39]:
In [40]:
count = np.squeeze(np.asarray(count_mat))
word_count = list(zip(words, count))
In [41]:
word_count[:10]
Out[41]:
In [42]:
word_count = sorted(word_count, key=lambda t:t[1], reverse=True)
In [44]:
word_count[:5]
Out[44]:
In [45]:
# 20번 이상 언급됐던 부분만 나오게 하기 ( 내용이 너무 많다.)
word_count2 = []
for item in word_count:
if item[1] > 20:
word_count2.append(item)
In [46]:
import matplotlib.pyplot as plt
from wordcloud import WordCloud
In [47]:
wc = WordCloud(font_path='C:\\Windows\\Fonts\\malgun.ttf', background_color='white', width=400, height=300)
cloud = wc.generate_from_frequencies(dict(word_count2))
plt.figure(figsize=(12,9))
plt.imshow(cloud)
plt.axis('off')
plt.show()
'BIGDATA > TEXT MINING' 카테고리의 다른 글
| [Crawling] Beautifulsoup & Requests (Crawling) (0) | 2017.09.11 |
|---|---|
| [TEXT MINING] ENCODING, 인코딩 (0) | 2017.09.11 |
| [URL] URL 분해 및 URL Encoding (0) | 2017.09.10 |
| [Crawling] Web Crawling(크롤링) (2) | 2017.09.10 |
| [TEXT MINING] 텍스트마이닝의 기초 (TDM) (0) | 2017.09.10 |
