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Hello, today i want to make a Wordcloud from Google Play reviews, I'd make explanation about wordcloud in detail, You can check right here: Wordcloud, I also teach you how to make wordcloud that match the shape we desire, however i want to make Wordcloud from Google Play reviews, we can use this if we have an App and we want to make Sentiment analysis to improve our app that we're developing.

Let us get started!
First we must import the module we need, here i used Google Colab so i need to import from google drive as an additional module to make things convenient


# Contoh teks
teks_asli = "Ini Adalah Contoh Teks yang Akan Dikonversi Menjadi Lowercase."

# Mengubah teks menjadi lowercase
teks_lowercase = teks_asli.lower()

# Menampilkan hasil
print("Teks asli:", teks_asli)
print("Teks setelah diubah menjadi lowercase:", teks_lowercase)

XXX Lorem Ipsum is simply dummy text of the printing and typesetting industry.

There is a module for scraping google play review, however it wasn't installed on the jupyter book yet so we need to install the module first: !pip install google-play-scraper
Then we import to scrape module: from google_play_scraper import app, reviews, Sort, reviews_all

  • app : allow to to scrape the detail off single app (Title, Install, Rating, etc.)
  • reviews: Fetch a limited batch of reviews with pagination (You can choose sort order, country, etc.)
  • Sort: Gives sorting option for reviews
Pandas to manipulate data Numpy to do numerical operation (Arrays) Matplotlib to plot a visual graphic


from google.colab import drive
drive.mount('/content/drive') 

Here i want to scrape a music app named Spotify because a lot of people use it:

scrapreviews, _ = reviews(
    'com.spotify.music', #important to find the Package ID of an app
    lang='en',
    country='gb',
    sort=Sort.NEWEST,
    count=1000
)
Here i only want to import 1000 with count=1000 instead of using reviews_all() because it will take alot of time

lang='en': sets the language to English
country='gb': Here i select the region GB or Great Britain 
sort=Sort.NEWEST: sort the newest reviews
Then we type to scrapreviews shows the result, the result is so long and could be different because this sort the newest review so be in mind Here i want to saves the result to csv to save the result i have,

app_reviews_df = pd.DataFrame(scrapreviews)
app_reviews_df.shape
app_reviews_df.head()
app_reviews_df.to_csv('customer_review_of_spotify.csv', index=False)

# Membuat DataFrame dari hasil scrapreview

# Menghitung jumlah baris dan kolom dalam DataFrame
reviews_number, columns_number = app_reviews_df.shape
print (reviews_number, columns_number )
app_reviews_df = pd.DataFrame(scrapreviews): app_reviews_df is a new variable, and we create new dataframe from the app_reviews_df.shape: To see the shape of the dataframe app_reviews_df.head(): This code is used quickly to see first rows app_reviews_df.to_csv('customer_review_of_spotify.csv', index=False): Save the CSV using pandas reviews_number, columns_number = app_reviews_df.shape print (reviews_number, columns_number ): In here the app_reviews_df.shape return tuple so we make the 2 new variable according to the tuple order so we get colums and rows number, in here i change the name "rows" to "reviews" Then i type to see the result of the dataframe we have, again, same as when i type scrapreviews to show the result please be in mind that the result would be different
Then we will make wordcloud from the CSV we have

# Creating word_cloud with text as argument in .generate() method

text1 = " ".join(title for title in app_reviews_df.content if title is not None)
word_cloud1 = WordCloud(collocations = False, background_color = 'white',
                        width = 2048, height = 1080).generate(text1)
word_cloud1.to_file('/content/drive/Mydrive/spotify_wordcloud.png')




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Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been ther took It has survived not only five centuries, but also the leap into electronic

Amelia Alex

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic but also the leap into electronic.

Amelia Alex

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic but also the leap into electronic.

Amelia Alex

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic but also the leap into electronic.

Comments

James Boreego
Reply
Dec 9, 2024

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic

James Boreego
Reply
Dec 9, 2024

Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy Lorem Ipsum has been the industry's standard dummy type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic

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