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import pandas as pd
import requests
import urllib.parse as urlparse
def get_video_id(url_video):
"""Get video id"""
query = urlparse.urlparse(url_video)
if query.hostname == 'youtu.be':
return query.path[1:]
if query.hostname in ('www.youtube.com', 'youtube.com'):
if query.path == '/watch':
return urlparse.parse_qs(query.query)["v"][0]
if query.path[:7] == '/embed/' or query.path[:3] == '/v/':
return query.path.split('/')[2]
return None
def get_comments(api_key, video_id):
"""Get comments"""
endpoint = "https://www.googleapis.com/youtube/v3/commentThreads"
params = {
"part":"snippet",
"videoId": video_id,
"maxResults": 100,
"key": api_key,
}
response = requests.get(endpoint, params=params)
res = response.json()
if "items" in res.keys():
return {
num: {
"text_comment": " ".join(
x["snippet"]["topLevelComment"]["snippet"][
"textOriginal"
].splitlines()
),
"publish_data": x["snippet"]["topLevelComment"]["snippet"][
"publishedAt"
],
}
for num, x in enumerate(res['items'])
}
def get_sentim(data, model):
"""Get result of sentimental analysis"""
res = model(data)[0]
return res['label'], res['score']
def pipeline_sentiment(url_video, api_key, model):
"""Pipeline of sentimental analysis"""
video_id = get_video_id(url_video)
comments = get_comments(api_key, video_id)
comments_df = pd.DataFrame(comments).T
text_tuple = [get_sentim(i, model) for i in comments_df["text_comment"]]
comments_df[["sentiment", "score"]] = pd.DataFrame(list(text_tuple))
return comments_df
def pipeline_stats(data):
"""Get statistic of sentiment"""
return data['sentiment'].value_counts(normalize=True) * 100
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