Crawling question using Python selenium.

num_of_end = 4

while num_of_end:
    body.send_keys(Keys.END)
    time.sleep(2)
    num_of_end-= 1

from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
from urllib.request import urlopen
import re
import pandas as pd
import numpy as np
import nltk
import seaborn as sns
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import string
import time

driver = webdriver.Chrome("C:\\Users\\AppData\\Roaming\\Microsoft\\Windows\\Start Menu\\Programs\\Python 3.8\\chromedriver")

# URL of the page to load
driver.get('https://www.youtube.com/watch?v=jv1b2c3EYb4')
time.sleep(1)

#Mouse cursor positioning. Position the mouse cursor on the body for scrolling
body = driver.find_element_by_tag_name("body")
html = driver.page_source
soup = BeautifulSoup(html, "html.parser")

# Import Title
title = driver.find_element_by_xpath('//*[@id="container"]/h1/yt-formatted-string').text
print("Video Title: " + title + '\n\n')

# Position scroll bar at bottom to collect full comments

#Specify the number of times you want to drop the page
num_of_end = 4

while num_of_end:
    body.send_keys(Keys.END)
    time.sleep(2)
    num_of_end-= 1


# Comments (extracted by html tag)
comment = soup.find_all('yt-formatted-string', {'id':'content-text'})

comment_list = []
for c in comment:
    comment_list.append(c.get_text().strip())

# Comment author id
user_id = soup.find_all('a', {'id':'author-text'})
id_list = []
for u in user_id:
    id_list.append(u.get_text().strip())

# I like it in the comments Number
like = soup.find_all('span', {'id':'vote-count-left'})

like_list_bf = []
like_list = []
for l in like:
    like_list_bf.append(l.get_text().strip())

#Replace empty value with zero
for bf in like_list_bf :
    if bf =='' :
        like_list.append(0)
    else :
        like_list.append(bf)


# Check the list of imported comments
for c in comment_list:
    print(c + '\n')

    # Verify that the size is constant to create a dataframe
print(len(comment_list))
print(len(id_list))
print(len(like_list))

# Pre-processing to prevent breakage when saving as csv (remove emoji-unicode, Arabic characters, etc.)
s_filter = re.compile("[^"
                        "a-zA-Z"      #English
                        "G-E-G-H-H" #Korean
                        "0-9"         #Number
                        "\{\}\[\]\/?.,;:|\)*~`!^\-_+<>@\#$%&\\=\(\'\"" #Special symbol
                        "\ "\ " #space
                        "]+")
# Pre-processing of comments
comment_result = []
for i in comment_list:
    i = re.sub(s_filter,"",i)
    i = ''.join(i)
    comment_result.append(i)

# The ID needs to be preprocessed because the ID can be used with Arabic characters
id_result = []
for i in id_list:
    i = re.sub(s_filter,"",i)
    i = ''.join(i)
    id_result.append(i)
    # Create each imported data into a single data frame
DB = pd.DataFrame({'id' : id_result,'comment' : comment_result,'like' : like_list})

# Add comment length for analysis
DB['text_length'] = DB['comment'].apply(len)

# Check DB
DB.head()

# Export to a csv file
DB.to_csv("Dataset Raw27.csv",encoding="euc-kr")