Spaces:
Build error
Build error
File size: 8,821 Bytes
b0e7079 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 |
import os
import re
import time
import requests
import ast
import pickle
import json
import torch
import pandas as pd
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import WebDriverWait
from langdetect import detect
from nepali_unicode_converter.convert import Converter
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.action_chains import ActionChains
# dataset = pd.read_csv("/media/gpu/157/Nepali_sentiment_Analysis/collected_labeled_data.csv")
review_url = "https://my.daraz.com.np/pdp/review/getReviewList?itemId=_id_&pageSize=5&filter=0&sort=0&pageNo=1"
model = pickle.load(open('bert_model/model','rb'))
tokenizers = pickle.load(open('tokenizers.pkl','rb'))
svc_sentiment = pickle.load(open('scv_sentiment','rb'))
chrome_options = Options()
chrome_options.add_argument("--headless")
def remove_emojis(text):
emoji_pattern = re.compile("["
u"\U0001F600-\U0001F64F" # emoticons
u"\U0001F300-\U0001F5FF" # symbols & pictographs
u"\U0001F680-\U0001F6FF" # transport & map symbols
u"\U0001F1E0-\U0001F1FF" # flags (iOS)
u"\U00002500-\U00002BEF" # chinese char
u"\U00002702-\U000027B0"
u"\U00002702-\U000027B0"
u"\U000024C2-\U0001F251"
u"\U0001f926-\U0001f937"
u"\U00010000-\U0010ffff"
u"\u2640-\u2642"
u"\u2600-\u2B55"
u"\u200d"
u"\u23cf"
u"\u23e9"
u"\u231a"
u"\ufe0f" # dingbats
u"\u3030"
"]+", re.UNICODE)
text = emoji_pattern.sub(r'', text)
return text
def get_bert_embedding_sentence(input_sentence):
md = model
tokenizer = tokenizers
marked_text = " [CLS] " + input_sentence + " [SEP] "
tokenized_text = tokenizer.tokenize(marked_text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
segments_ids = [1] * len(indexed_tokens)
tokens_tensors = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])
with torch.no_grad():
outputs = md(tokens_tensors, segments_tensors)
hidden_states = outputs.hidden_states
token_vecs = hidden_states[-2][0]
sentence_embedding = torch.mean(token_vecs, dim=0)
return sentence_embedding.numpy()
def scrap_data():
positive_sentimet = dataset.loc[dataset['label'] == 1]
negative_sentiment = dataset.loc[dataset['label'] == 0]
return positive_sentimet, negative_sentiment
def comment_sentiment(text):
lang_list = ["hi","ne","mr"]
converter = Converter()
if detect(text) == "ne":
embedding = get_bert_embedding_sentence(text)
svc_pred = svc_sentiment.predict(embedding.reshape(1,-1))[0]
"""
if detect(text) not in lang_list:
result = converter.convert(text)
embedding = get_bert_embedding_sentence(result)
svc_pred = svc_sentiment.predict(embedding.reshape(1,-1))[0]
# predicted_label.append(svc_pred)
# comment_text.append(review["reviewContent"])
else:
embedding = get_bert_embedding_sentence(text)
svc_pred = svc_sentiment.predict(embedding.reshape(1,-1))[0]
# predicted_label.append(svc_pred)
# comment_text.append(review["reviewContent"])
"""
return svc_pred
def scrape_comment(url):
lang_list = ["hi","ne","mr"]
converter = Converter()
id = url.split("-")[-2].replace("i","")
api_url = review_url.replace("_id_",id)
print("---------------------------------")
response = requests.get(api_url).text
print(response)
response = json.loads(response)
df = pd.DataFrame(columns=["text",'label'])
reviews = response["model"]["items"]
predicted_label =[]
comment_text =[]
for review in reviews:
text = review["reviewContent"]
try:
if detect(text) not in lang_list:
result = converter.convert(text)
embedding = get_bert_embedding_sentence(result)
svc_pred = svc_sentiment.predict(embedding.reshape(1,-1))[0]
predicted_label.append(svc_pred)
comment_text.append(review["reviewContent"])
else:
embedding = get_bert_embedding_sentence(text)
svc_pred = svc_sentiment.predict(embedding.reshape(1,-1))[0]
predicted_label.append(svc_pred)
comment_text.append(review["reviewContent"])
except Exception as e:
print(e)
pass
df['text'] = comment_text
df['label'] = predicted_label
positive_sentimet = df.loc[df['label'] == 1]
negative_sentiment = df.loc[df['label'] == 0]
return positive_sentimet, negative_sentiment
# def scrap_twitter(url):
# tweets = driver.find_elements(By.XPATH,'//*[@id="id__nspdargek9"]/span/text()')
# print(tweets)
def scrape_twitter(url):
'''
to scrape tweet from given username provide username and tweet id
'''
driver = webdriver.Chrome("driver/chromedriver",options=chrome_options)
# driver.get(f"https://twitter.com/{username}/status/{tweet_id}")
driver.get(url)
time.sleep(5) #change according to your pc and internet connection
tweets = []
result = False
old_height = driver.execute_script("return document.body.scrollHeight")
#set initial all_tweets to start loop
all_tweets = driver.find_elements(By.XPATH, '//div[@data-testid]//article[@data-testid="tweet"]')
while result == False:
for item in all_tweets[1:]: # skip tweet already scrapped
try:
text = item.find_element(By.XPATH, './/div[@data-testid="tweetText"]').text
except:
text = '[empty]'
#Append new tweets replies to tweet array
tweets.append(text)
#scroll down the page
driver.execute_script("window.scrollTo(0,document.body.scrollHeight)")
time.sleep(2)
try:
try:
button = driver.find_element_by_css_selector("div.css-901oao.r-1cvl2hr.r-37j5jr.r-a023e6.r-16dba41.r-rjixqe.r-bcqeeo.r-q4m81j.r-qvutc0")
except:
button = driver.find_element_by_css_selector("div.css-1dbjc4n.r-1ndi9ce") #there are two kinds of buttons
ActionChains(driver).move_to_element(button).click(button).perform()
time.sleep(2)
driver.execute_script("window.scrollTo(0,document.body.scrollHeight)")
time.sleep(2)
except:
pass
new_height = driver.execute_script("return document.body.scrollHeight")
if new_height == old_height:
result = True
old_height = new_height
#update all_tweets to keep loop
all_tweets = driver.find_elements(By.XPATH, '//div[@data-testid]//article[@data-testid="tweet"]')
driver.close()
text = []
predicted_label = []
for comments in tweets:
try:
result = comment_sentiment(comments)
comments = remove_emojis(comments)
text.append(comments)
predicted_label.append(result)
except Exception as e:
pass
df = pd.DataFrame(columns=["text","label"])
df['text'] = text
df['label'] = predicted_label
positive_sentimet = df.loc[df['label'] == 1]
negative_sentiment = df.loc[df['label'] == 0]
return positive_sentimet, negative_sentiment
def scrape_youtube(url):
driver = webdriver.Chrome("driver/chromedriver",options=chrome_options)
data =[]
wait = WebDriverWait(driver,15)
driver.get(url)
predicted_label = []
for item in range(5):
wait.until(EC.visibility_of_element_located((By.TAG_NAME, "body"))).send_keys(Keys.END)
time.sleep(5)
for comment in wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, "#content"))):
data.append(comment.text)
text =[]
for comments in data:
try:
result =comment_sentiment(comments)
comments = remove_emojis(comments)
text.append(comments)
predicted_label.append(result)
except Exception as e:
# raise
pass
driver.close()
df = pd.DataFrame(columns=["text","label"])
df['text'] = text
df['label'] = predicted_label
positive_sentimet = df.loc[df['label'] == 1]
negative_sentiment = df.loc[df['label'] == 0]
return positive_sentimet, negative_sentiment
if __name__ == "__main__":
url = "https://www.youtube.com/watch?v=uD58-EHwaeI"
positive_sentimet, negative_sentiment= scrap_youtube(url=url)
print(positive_sentimet, negative_sentiment)
|