import os
import googleapiclient.discovery
# Fetching the data from Youtube API
def google_api(key,vidId):
os.environ["OAUTHLIB_INSECURE_TRANSPORT"] = "1"
api_service_name = "youtube"
api_version = "v3"
DEVELOPER_KEY = 'isi punya kamu'
youtube = googleapiclient.discovery.build(
api_service_name, api_version, developerKey = DEVELOPER_KEY)
request = youtube.commentThreads().list(
part="id,snippet",
maxResults=1000,
order="relevance",
videoId=vidId
)
response = request.execute()
return response
key = "isi punya kamu"
vidId = "id video youtube"
response = google_api(key,vidId)
print(response)
# Creation of Data Frame
import pandas as pd
def create_df_author_comments(response):
authorname = []
comments = []
for i in range(len(response["items"])):
authorname.append(response["items"][i]["snippet"]["topLevelComment"]["snippet"]["authorDisplayName"])
comments.append(response["items"][i]["snippet"]["topLevelComment"]["snippet"]["textOriginal"])
df_1 = pd.DataFrame(comments, index = authorname,columns=["Comments"])
return df_1
df = create_df_author_comments(response)
print(df)
# Cleaning Round 1
import re
def cleaning_comments(comment):
comment = re.sub("[🤣|🤭|🤣|😁|🤭|❤️|👍|🏴|😣|😠|💪|🙏]+",'',comment)
comment = re.sub("[0-9]+","",comment)
comment = re.sub("[\:|\@|\)|\*|\.|\$|\!|\?|\,|\%|\"]+"," ",comment)
return comment
df["Comments"]= df["Comments"].apply(cleaning_comments)
print(df)
# Cleaning Round 2
def cleaning_comments1(comment):
comment = re.sub("[💁|🌾|😎|♥|🤷♂]+","",comment)
comment = re.sub("[\(|\-|\”|\“|\#|\!|\/|\«|\»|\&]+","",comment)
return comment
df["Comments"] = df["Comments"].apply(cleaning_comments1)
print(df)
# Cleaning Round 3
def cleaning_comments3(comment):
comment = re.sub("\n|ini|dan|yang|kamu|saya|ane|dia|nya|sama|dan|ada|orang|jadi|itu|bisa|dari|juga|dengan|tidak|gw|kalau|jadi|bgt|lagi|yg|om|di|ya|ga|gak|dgn|ta|untuk|dulu|tp|sangat|apa|tau|ng|ke|dalam|ka"," ",comment)
comment = re.sub('[\'|🇵🇰|\;|\！]+','',comment)
return comment
df["Comments"] = df["Comments"].apply(cleaning_comments3)
print(df)
#Cleaning Round 4
lower = lambda comment: comment.lower()
df['Comments'] = df['Comments'].apply(lower)
print(df)
# Final DataFrame after cleaning
print(df)
#Removing Empty Comments
def remove_comments(df):
# Checks for comments which has zero length in a dataframe
zero_length_comments = df[df["Comments"].map(len) == 0]
# taking all the indexes of the filtered comments in a list
zero_length_comments_index = [ind for ind in zero_length_comments.index]
# removing those rows from dataframe whose indexes matches
df.drop(zero_length_comments_index, inplace = True)
return df
df = remove_comments(df)
df
from textblob.blob import TextBlob
#Finding Subjectivity
def find_subjectivity_on_single_comment(text):
return TextBlob(text).sentiment.subjectivity
def apply_subjectivity_on_all_comments(df):
df['Subjectivity'] = df['Comments'].apply(find_subjectivity_on_single_comment)
return df
df = apply_subjectivity_on_all_comments(df)
df
#Finding Polarity
def find_polarity_of_single_comment(text):
return TextBlob(text).sentiment.polarity
def find_polarity_of_every_comment(df):
df['Polarity'] = df['Comments'].apply(find_polarity_of_single_comment)
return df
df = find_polarity_of_every_comment(df)
df
# Analysis Based on Polarity
analysis = lambda polarity: 'Positive' if polarity > 0 else 'Neutral' if polarity == 0 else 'Negative'
def analysis_based_on_polarity(df):
df['Analysis'] = df['Polarity'].apply(analysis)
return df
df = analysis_based_on_polarity(df)
df
# Printing Positive Comments
def print_positive_comments():
print('Printing positive comments:\n')
sortedDF = df.sort_values(by=['Polarity'])
for i in range(0, sortedDF.shape[0] ):
if( sortedDF['Analysis'][i] == 'Positive'):
print(str(i+1) + '> '+ sortedDF['Comments'][i])
print()
print_positive_comments()
# Printing Negative Comments
def print_negative_comments():
print('Printing negative comments:\n')
sortedDF = df.sort_values(by=['Polarity'])
for i in range(0, sortedDF.shape[0] ):
if( sortedDF['Analysis'][i] == 'Negative'):
print(str(i+1) + '> '+ sortedDF['Comments'][i])
print()
print_negative_comments()
# Printing Neutral Comments
def print_neutral_comments():
print('Printing neutral comments:\n')
sortedDF = df.sort_values(by=['Polarity'])
for i in range(0, sortedDF.shape[0] ):
if( sortedDF['Analysis'][i] == 'Neutral'):
print(str(i+1) + '> '+ sortedDF['Comments'][i])
print()
print_neutral_comments()
# Forming WordClouds
import matplotlib.pyplot as plt
from sklearn.feature_extraction import text
from wordcloud import WordCloud
def generate_word_clouds(df):
allWords = ' '.join([twts for twts in df['Comments']])
wordCloud = WordCloud(stopwords = text.ENGLISH_STOP_WORDS ,width=2500, height=2000, background_color='white').generate(allWords)
plt.imshow(wordCloud)
plt.axis('off')
plt.show()
generate_word_clouds(df)
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