def get_app_store(app_id, n_reviews=200, filter="all"):
"""get play store reviews for app"""
data = AppStore(app_name=app_id, country='us')
data.review(how_many=n_reviews)
reviews = data.reviews
reviews_df = pd.DataFrame(reviews)
if filter == "all":
return reviews_df
else:
return reviews_df.query('rating' + filter)
def scrape_data():
app_info = AppStore(
app_name=input("Enter name of the app in the App Store: "),
country="in")
app_info.review(how_many=3000)
reviews = list(
map(lambda review: normalize_review(review), app_info.reviews))
f = open("../app-store-reviews.json", "w")
f.write(json.dumps(reviews))
f.close()
def Crawl_appsList(l):
dataTable = []
for i, app in enumerate(l):
try:
application = AppStore(country="eg", app_name=app)
application.review()
tupl = Crawl_app(application, application.reviews)
dataTable.append(tupl)
except:
print("Couldn't found App: ", app, "\nCheck the application name again.")
continue
return dataTable
import csv
from app_store_scraper import AppStore
# Change this to the name of the csv file containing your app metadata
# requires "url_name" and "id" fields
IN_FILE = 'as_eating_disorder_apps.csv'
# read app name and id from csv file
apps = []
with open(IN_FILE,newline='',encoding='utf-8') as file:
reader = csv.DictReader(file)
for row in reader:
apps.append({'app_name':row['url_name'],'app_id':row['id']})
# for each app, use the AppStore scraper to fetch all reviews
for app in apps:
app_temp = AppStore(country='gb', app_name=app['app_name'], app_id=app['app_id'])
app_temp.review()
app['reviews'] = app_temp.reviews # save list of reviews to existing dictionary
# save reviews to separate csv files for each app
for app in apps:
if app.get('reviews'): # Not all apps have reviews
outfile = app['app_name'] + '_reviews.csv' # use the app_name to name the output file
with open(outfile,'w',encoding='utf-8',newline='') as file:
# Needed to add developerResponse by hand as only some reviews have this field
dict_writer = csv.DictWriter(file, list(app['reviews'][0].keys()) + ['developerResponse'])
dict_writer.writeheader()
dict_writer.writerows(app['reviews'])
class AppReview(TopicModeller):
def __init__(self, country, app_name):
self.country = country
self.app_name = app_name
self.review_unit = AppStore(country=country, app_name=app_name)
self.topic_reviews = pd.DataFrame()
def get_reviews(self, num_reviews=None):
'''
Returns the reviews as a pandas
dataframe.
'''
review_dict = {
'title': [],
'rating': [],
'userName': [],
'review': [],
'date': [],
'isEdited': []
}
if num_reviews:
self.review_unit.review(how_many=num_reviews)
else:
self.review_unit.review()
for review in self.review_unit.reviews:
review_dict['title'].append(review['title'])
review_dict['rating'].append(review['rating'])
review_dict['userName'].append(review['userName'])
review_dict['review'].append(review['review'])
review_dict['date'].append(
review['date'].strftime("%m/%d/%Y, %H:%M:%S"))
review_dict['isEdited'].append(review['isEdited'])
self.review_df = pd.DataFrame(data=review_dict)
return self.review_df
def generate_embeddings(self):
'''
Generates embeddings based on
reviews from app store
'''
df_dict = {'sentence': []}
for idx, c in enumerate(self.review_df.review):
df_dict['sentence'].append(c)
df_pd = pd.DataFrame(data=df_dict)
super().__init__(df_pd)
def cluster_embeddings(self, num_topics=None):
self.num_topics = num_topics
self.topic_reviews = super().project(topics=num_topics)
def plot_embeddings(self, port_num=9000):
super().plot(self.app_name, port_num=port_num)
def generate_topic_csv(self, csv_path):
generate_csv(self.topic_reviews, csv_path, self.num_topics)
def get_reviews(app_name):
app_search = AppStore(country="in", app_name=app_name)
app_search.review(how_many=50000)
return app_search.reviews
import json
from app_store_scraper import AppStore
arrivecan = AppStore(country="ca", app_name="arrivecan")
arrivecan.review()
for i in arrivecan.reviews:
i['date'] = i['date'].strftime("%m/%d/%Y, %H:%M:%S")
with open('apple.json', 'w', encoding='utf-8') as f:
json.dump(arrivecan.reviews, f, ensure_ascii=False, indent=4)
# config['app_id']['uber_apple'],
config['app_id']['blablacar_apple'],
# config['app_id']['cabify_apple'],
# config['app_id']['via_apple'],
# config['app_id']['getaround_apple'],
# config['app_id']['olacabs_apple'],
# config['app_id']['taxieu_apple'],
# config['app_id']['freenow_apple'],
# config['app_id']['yandexgo_apple']
]
list_of_country_code = config['country_code']
output_path = config['output_path']
for app_id in list_of_app_id:
df_merged = None
for country_code in list_of_country_code:
appstore = AppStore(country=country_code, app_name=app_id)
appstore.review(how_many=100000)
if df_merged is not None:
df = pd.json_normalize(appstore.reviews)
df_merged = df_merged.append(df)
else:
df_merged = pd.json_normalize(appstore.reviews)
csv_file_name = app_id + '_apple_appstore_review.csv'
df_merged.to_csv(output_path + csv_file_name)