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kashmir_times.py
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kashmir_times.py
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"""
In this script, we will be extracting the news from the given url. The news will be stored in a dataframe file.
The News are from the given url.
-TheNews
-Hindustan Times
-BBC News
-Economic Times
-The Hindu
-The Indian Express
-The Times of India
-The Tribune
-The Wire
-Eurasian Times, India
-The New Indian Express
-The Print, India
Input:
url: The url of the news website
Output:
news_df: The dataframe file of the news
"""
# import the required modules and libraries
import en_core_web_sm
from nltk.tokenize import word_tokenize, sent_tokenize
from datetime import datetime, date, time
import time
from time import ctime
import re
import requests
from bs4 import BeautifulSoup, SoupStrainer
import pandas as pd
# nltk.download()
from nltk import word_tokenize
# from nltk.corpus import stopwords
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.stem.porter import PorterStemmer
from spacy.lang.en.stop_words import STOP_WORDS
from gensim.parsing.preprocessing import remove_stopwords
import string
from spacy.lang.en import English
import utils as utils
# from practice import news_authors, news_title
punctuations = string.punctuation
nlp = English()
nlp = en_core_web_sm.load()
# stop_words = set(stopwords.words('english'))
nlp.add_pipe('sentencizer') # updated
parser = English()
stopwords = ["1qfy23", "eu", "t", "and", "s", "â€", "0o", "0s", "3a", "3b", "3d", "6b", "6o", "a", "a1", "a2", "a3",
"a4", "ab", "able", "about", "above", "abst", "ac", "accordance", "according", "accordingly", "across",
"act", "actually", "ad", "added", "adj", "ae", "af", "affected", "affecting", "affects", "after",
"afterwards", "ag", "again", "against", "ah", "ain", "ain't", "aj", "al", "all", "allow", "allows",
"almost", "alone", "along", "already", "also", "although", "always", "am", "among", "amongst", "amoungst",
"amount", "an", "and", "announce", "another", "any", "anybody", "anyhow", "anymore", "anyone", "anything",
"anyway", "anyways", "anywhere", "ao", "ap", "apart", "apparently", "appear", "appreciate", "appropriate",
"approximately", "ar", "are", "aren", "arent", "aren't", "arise", "around", "as", "a's", "aside", "ask",
"asking", "associated", "at", "au", "auth", "av", "available", "aw", "away", "awfully", "ax", "ay", "az",
"b", "b1", "b2", "b3", "ba", "back", "bc", "bd", "be", "became", "because", "become", "becomes",
"becoming", "been", "before", "beforehand", "begin", "beginning", "beginnings", "begins", "behind",
"being", "believe", "below", "beside", "besides", "best", "better", "between", "beyond", "bi", "bill",
"biol", "bj", "bk", "bl", "bn", "both", "bottom", "bp", "br", "brief", "briefly", "bs", "bt", "bu", "but",
"bx", "by", "c", "c1", "c2", "c3", "ca", "call", "came", "can", "cannot", "cant", "can't", "cause",
"causes", "cc", "cd", "ce", "certain", "certainly", "cf", "cg", "ch", "changes", "ci", "cit", "cj", "cl",
"clearly", "cm", "c'mon", "cn", "co", "com", "come", "comes", "con", "concerning", "consequently",
"consider", "considering", "contain", "containing", "contains", "corresponding", "could", "couldn",
"couldnt", "couldn't", "course", "cp", "cq", "cr", "cry", "cs", "c's", "ct", "cu", "currently", "cv", "cx",
"cy", "cz", "d", "d2", "da", "date", "dc", "dd", "de", "definitely", "describe", "described", "despite",
"detail", "df", "di", "did", "didn", "didn't", "different", "dj", "dk", "dl", "do", "does", "doesn",
"doesn't", "doing", "don", "done", "don't", "down", "downwards", "dp", "dr", "ds", "dt", "du", "due",
"during", "dx", "dy", "e", "e2", "e3", "ea", "each", "ec", "ed", "edu", "ee", "ef", "effect", "eg", "ei",
"eight", "eighty", "either", "ej", "el", "eleven", "else", "elsewhere", "em", "empty", "en", "end",
"ending", "enough", "entirely", "eo", "ep", "eq", "er", "es", "especially", "est", "et", "et-al", "etc",
"eu", "ev", "even", "ever", "every", "everybody", "everyone", "everything", "everywhere", "ex", "exactly",
"example", "except", "ey", "f", "f2", "fa", "far", "fc", "few", "ff", "fi", "fifteen", "fifth", "fify",
"fill", "find", "fire", "first", "five", "fix", "fj", "fl", "fn", "fo", "followed", "following", "follows",
"for", "former", "formerly", "forth", "forty", "found", "four", "fr", "from", "front", "fs", "ft", "fu",
"full", "further", "furthermore", "fy", "g", "ga", "gave", "ge", "get", "gets", "getting", "gi", "give",
"given", "gives", "giving", "gj", "gl", "go", "goes", "going", "gone", "got", "gotten", "gr", "greetings",
"gs", "gy", "h", "h2", "h3", "had", "hadn", "hadn't", "happens", "hardly", "has", "hasn", "hasnt",
"hasn't", "have", "haven", "haven't", "having", "he", "hed", "he'd", "he'll", "hello", "help", "hence",
"her", "here", "hereafter", "hereby", "herein", "heres", "here's", "hereupon", "hers", "herself", "hes",
"he's", "hh", "hi", "hid", "him", "himself", "his", "hither", "hj", "ho", "home", "hopefully", "how",
"howbeit", "however", "how's", "hr", "hs", "http", "hu", "hundred", "hy", "i", "i2", "i3", "i4", "i6",
"i7", "i8", "ia", "ib", "ibid", "ic", "id", "i'd", "ie", "if", "ig", "ignored", "ih", "ii", "ij", "il",
"i'll", "im", "i'm", "immediate", "immediately", "importance", "important", "in", "inasmuch", "inc",
"indeed", "index", "indicate", "indicated", "indicates", "information", "inner", "insofar", "instead",
"interest", "into", "invention", "inward", "io", "ip", "iq", "ir", "is", "isn", "isn't", "it", "itd",
"it'd", "it'll", "its", "it's", "itself", "iv", "i've", "ix", "iy", "iz", "j", "jj", "jr", "js", "jt",
"ju", "just", "k", "ke", "keep", "keeps", "kept", "kg", "kj", "km", "know", "known", "knows", "ko", "l",
"l2", "la", "largely", "last", "lately", "later", "latter", "latterly", "lb", "lc", "le", "least", "les",
"less", "lest", "let", "lets", "let's", "lf", "like", "liked", "likely", "line", "little", "lj", "ll",
"ll", "ln", "lo", "look", "looking", "looks", "los", "lr", "ls", "lt", "ltd", "m", "m2", "ma", "made",
"mainly", "make", "makes", "many", "may", "maybe", "me", "mean", "means", "meantime", "meanwhile",
"merely", "mg", "might", "mightn", "mightn't", "mill", "million", "mine", "miss", "ml", "mn", "mo", "more",
"moreover", "most", "mostly", "move", "mr", "mrs", "ms", "mt", "mu", "much", "mug", "must", "mustn",
"mustn't", "my", "myself", "n", "n2", "na", "name",
"namely", "nay", "nc", "nd", "ne", "near", "nearly", "necessarily", "necessary", "need", "needn",
"needn't", "needs", "neither", "never", "nevertheless", "new", "next", "ng", "ni", "nine", "ninety", "nj",
"nl", "nn", "no", "nobody", "non", "none", "nonetheless", "noone", "nor", "normally", "nos", "not",
"noted", "nothing", "novel", "now", "nowhere", "nr", "ns", "nt", "ny", "o", "oa", "ob", "obtain",
"obtained", "obviously", "oc", "od", "of", "off", "often", "og", "oh", "oi", "oj", "ok", "okay", "ol",
"old", "om", "omitted", "on", "once", "one", "ones", "only", "onto", "oo", "op", "oq", "or", "ord", "os",
"ot", "other", "others", "otherwise", "ou", "ought", "our", "ours", "ourselves", "out", "outside", "over",
"overall", "ow", "owing", "own", "ox", "oz", "p", "p1", "p2", "p3", "page", "pagecount", "pages", "par",
"part", "particular", "particularly", "pas", "past", "pc", "pd", "pe", "per", "perhaps", "pf", "ph", "pi",
"pj", "pk", "pl", "placed", "please", "plus", "pm", "pn", "po", "poorly", "possible", "possibly",
"potentially", "pp", "pq", "pr", "predominantly", "present", "presumably", "previously", "primarily",
"probably", "promptly", "proud", "provides", "ps", "pt", "pu", "put", "py", "q", "qj", "qu", "que",
"quickly", "quite", "qv", "r", "r2", "ra", "ran", "rather", "rc", "rd", "re", "readily", "really",
"reasonably", "recent", "recently", "ref", "refs", "regarding", "regardless", "regards", "related",
"relatively", "research", "research-articl", "respectively", "resulted", "resulting", "results", "rf",
"rh", "ri", "right", "rj", "rl", "rm", "rn", "ro", "rq", "rr", "rs", "rt", "ru", "run", "rv", "ry", "s",
"s2", "sa", "said", "same", "saw", "say", "saying", "says", "sc", "sd", "se", "sec", "second", "secondly",
"section", "see", "seeing", "seem", "seemed", "seeming", "seems", "seen", "self", "selves", "sensible",
"sent", "serious", "seriously", "seven", "several", "sf", "shall", "shan", "shan't", "she", "shed",
"she'd", "she'll", "shes", "she's", "should", "shouldn", "shouldn't", "should've", "show", "showed",
"shown", "showns", "shows", "si", "side", "significant", "significantly", "similar", "similarly", "since",
"sincere", "six", "sixty", "sj", "sl", "slightly", "sm", "sn", "so", "some", "somebody", "somehow",
"someone", "somethan", "something", "sometime", "sometimes", "somewhat", "somewhere", "soon", "sorry",
"sp", "specifically", "specified", "specify", "specifying", "sq", "sr", "ss", "st", "still", "stop",
"strongly", "sub", "substantially", "successfully", "such", "sufficiently", "suggest", "sup", "sure", "sy",
"system", "sz", "t", "t1", "t2", "t3", "take", "taken", "taking", "tb", "tc", "td", "te", "tell", "ten",
"tends", "tf", "th", "than", "thank", "thanks", "thanx", "that", "that'll", "thats", "that's", "that've",
"the", "their", "theirs", "them", "themselves", "then", "thence", "there", "thereafter", "thereby",
"thered", "therefore", "therein", "there'll", "thereof", "therere", "theres", "there's", "thereto",
"thereupon", "there've", "these", "they", "theyd", "they'd", "they'll", "theyre", "they're", "they've",
"thickv", "thin", "think", "third", "this", "thorough", "thoroughly", "those", "thou", "though", "thoughh",
"thousand", "three", "throug", "through", "throughout", "thru", "thus", "ti", "til", "tip", "tj", "tl",
"tm", "tn", "to", "together", "too", "took", "top", "toward", "towards", "tp", "tq", "tr", "tried",
"tries", "truly", "try", "trying", "ts", "t's", "tt", "tv", "twelve", "twenty", "twice", "two", "tx", "u",
"u201d", "ue", "ui", "uj", "uk", "um", "un", "under", "unfortunately", "unless", "unlike", "unlikely",
"until", "unto", "uo", "up", "upon", "ups", "ur", "us", "use", "used", "useful", "usefully", "usefulness",
"uses", "using", "usually", "ut", "v", "va", "value", "various", "vd", "ve", "ve", "very", "via", "viz",
"vj", "vo", "vol", "vols", "volumtype", "vq", "vs", "vt", "vu", "w", "wa", "want", "wants", "was", "wasn",
"wasnt", "wasn't", "way", "we", "wed", "we'd", "welcome", "well", "we'll", "well-b", "went", "were",
"we're", "weren", "werent", "weren't", "we've", "what", "whatever", "what'll", "whats", "what's", "when",
"whence", "whenever", "when's", "where", "whereafter", "whereas", "whereby", "wherein", "wheres",
"where's", "whereupon", "wherever", "whether", "which", "while", "whim", "whither", "who", "whod",
"whoever", "whole", "who'll", "whom", "whomever", "whos", "who's", "whose", "why", "why's", "wi", "widely",
"will", "willing", "wish", "with", "within", "without", "wo", "won", "wonder", "wont", "won't", "words",
"world", "would", "wouldn", "wouldnt", "wouldn't", "www", "x", "x1", "x2", "x3", "xf", "xi", "xj", "xk",
"xl", "xn", "xo", "xs", "xt", "xv", "xx", "y", "y2", "yes", "yet", "yj", "yl", "you", "youd", "you'd",
"you'll", "your", "youre", "you're", "yours", "yourself", "yourselves", "you've", "yr", "ys", "yt", "z",
"zero", "zi", "zz", ] + list(STOP_WORDS)
main_url = 'http://www.kashmirtimes.com/'
class KashmirTimes_News():
"""
This class will be used to scrap the news from the given url.
"""
# KashmirTimes
def __init__(self, ):
pass
# self.url = url
def HTML_Document(self, url):
# request for HTML document of given url
agent = {
"User-Agent": 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/59.0.3071.115 Safari/537.36'}
# request for HTML document of given url
try:
response = requests.get(url, headers=agent, verify=True)
html_document = response.text
return html_document
except:
return "Internet Connection Error"
def B_Soup(self, html_document):
soup = BeautifulSoup(html_document, 'html.parser')
# soup.prettify()
return soup
def News_Title(self, soup):
try:
# title = soup.find("title", ).text
title = soup.title.get_text(strip=True)
# if title:
# return title
# else:
# title = soup.find('h2').get_text(strip=True)
except:
title = "No Title"
return title
def News_Time(self, soup):
try:
time = soup.find('p', class_=["newsdate"]).get_text()
time = time.split(' ')[-2]
except:
now = datetime.now()
time = now.strftime("%H:%M:%S")
return time
def News_Date(self, soup):
try:
news_date = soup.find('p', class_=["newsdate"]).get_text()
news_date = news_date.split(' ')[-3]
except:
news_date = date.today()
return news_date
def News_Authors(self, soup):
try:
source = soup.find("meta", {"name": "author"}).attrs['content']
if source:
return source
else:
source = soup.find(
'a', class_=['story__byline__link']).get_text(strip=True)
except:
source = "No Source"
return source
def News_Author_Link(self, soup):
try:
source_link = soup.find(
"meta", {"property": "article:author"})['content']
if source_link:
return source_link
else:
source_link = soup.find(
'div', class_=['article-top-author-nw-nf-left']).get('href')
# source_link = source_link
except:
source_link = "No Source Link"
return source_link
def Image_URL(self, soup):
try:
image_src = soup.find('img', class_=["img-responsive", "pic"])
print(image_src['src'])
except:
image_src = None
return image_src
def Image_Alt_Text(self, soup):
try:
image_src = soup.find('img', class_=["img-responsive", "pic"])
print(image_src['src'])
except:
images_alt = None
return images_alt
def News_Detail(self, soup):
# try:
article_text = ''
try:
definition = soup.find('div', class_=['content count-br', 'story-detail'])
for p in definition.find_all('p'):
article_text = article_text + p.get_text(strip=True)
# article = soup.find('story-detail').find_all('p').text
# for paragraph in article.find_all('p'):
# article_text += paragraph.text
article_text = article_text.replace("\n", " ")
article_text = article_text.replace("\xa0", " ")
article_text = article_text.replace('?', '')
except:
article = soup.findAll('p')
for element in article:
article_text += '\n' + ''.join(element.findAll(text=True))
article_text = article_text.replace("\xa0", " ")
article_text = article_text.replace('?', '')
# print(article_text)
return article_text
def Short_Description(self, soup):
try:
short_desc = soup.find("meta", {"property": "og:description"})['content'].strip()
if short_desc is not None:
return short_desc
else:
short_desc = soup.find(
'h3', attrs={'class': 'preamble-nf'}).get_text(strip=True)
except:
short_desc = "No Short Description"
return short_desc
def News_Section(self, soup):
try:
news_section = soup.find(
'div', attrs={'class': 'secName'}).get_text(strip=True).title()
except:
news_section = "No Section"
return news_section
def Scrap_World_Links(self, url):
html_document = self.HTML_Document(url)
if html_document is not None:
soup = self.B_Soup(html_document)
all_links = []
results = soup.find_all('div', class_=['wpb_wrapper', ]) # 'full-light-container'
# results = soup.find_all('div',
# class_=['col-md-9 col-sm-12 col-xs-12 left-block', ]) # 'full-light-container'
# results_links = [i for i in results if i is not None]
for div in results:
links = div.findAll('a', href=True)
# a_tag.append(links)
for a in links:
if a['href'] and len(a['href']) > 55:
link = a['href']
all_links.append(link)
print(link)
else:
continue
all_links = [i for i in set(all_links)]
print("all Links here:", all_links)
return all_links
else:
print('connection error')
return None
def Scrap_Latest_Links(self, url):
html_document = self.HTML_Document(url)
if html_document is not None:
soup = self.B_Soup(html_document)
all_links = []
# results = soup.find_all('div', class_=['col-md-8 col-sm-8 col-xs-12 left-block', ])#'full-light-container'
results = soup.find_all('div',
class_=[
'col-lg-4 no1 topnews', ]) # 'col-lg-8 no right-sep main'
# results_links = [i for i in results if i is not None]
for div in results:
links = div.findAll('a', href=True)
# a_tag.append(links)
for a in links:
if a['href'] and len(a['href']) > 15:
link = main_url + a['href']
all_links.append(link)
print(link)
else:
continue
all_links = [i for i in set(all_links)]
print("all Links here:", all_links)
return all_links
else:
print('connection error')
return None
def Scrap_National_Links(self, url):
html_document = self.HTML_Document(url)
if html_document is not None:
soup = self.B_Soup(html_document)
all_links = []
results = soup.find_all('div', class_=['wpb_wrapper', ]) # 'full-light-container'
# results = soup.find_all('div',
# class_=['col-md-9 col-sm-12 col-xs-12 left-block', ]) # 'full-light-container'
# results_links = [i for i in results if i is not None]
for div in results:
links = div.findAll('a', href=True)
# a_tag.append(links)
for a in links:
if a['href'] and len(a['href']) > 60:
link = a['href']
all_links.append(link)
print(link)
else:
continue
all_links = [i for i in set(all_links)]
print("all Links here:", all_links)
return all_links
else:
print('connection error')
return None
def Scrap_KashmirTimes_News(self, article_urls, name, **kwargs):
df_news = {"News_URL": [], "News_Source": [], "News_Title": [], "News_Date": [], "News_Time": [],
"News_Authors": [], "News_Authors_Source": [], "News_Image_Link": [], "News_Image_Caption": [],
"News_Proper_Nouns": [], "News_Verbs": [], "News_Cardinal_Digit": [], "News_Target_Names": [],
"News_Total_Words": [], "News_Total_Summary_Words": [],
'News_Word_Cloud': [], 'News_Summary_Word_Cloud': [], "News_Short_Description": [],
"News_Detail": [], "News_Summary": [], "News_Polarity_Score": [],
"News_Subjectivity": [], "News_Sentiments": [], "News_Classification": [], "News_Section": [],
"News_Event": [], "News_Country": [], "News_City": [],
"News_Address": [], "News_Latitude": [], "News_Longitude": []}
# extract all links using the above function
# article_urls = self.Scrap_Links(link)
print("-------------------------------URL Scrapped from Requested Link-------------------------------")
print("-------------------------------Totatl URL's Scrapped from Requested Link: ", len(article_urls))
i = 0
for url in article_urls:
print("Scraping URL # ", i, "\nLink :", url)
i += 1
html = self.HTML_Document(url)
soup = self.B_Soup(html)
news_title = self.News_Title(soup)
news_authors = self.News_Authors(soup)
news_authors_link = self.News_Author_Link(soup)
news_time = self.News_Time(soup)
news_date = self.News_Date(soup)
news_short_desc = self.Short_Description(soup)
news_image_link = self.Image_URL(soup)
news_image_text = self.Image_Alt_Text(soup)
news_section = self.News_Section(soup)
news_text = self.News_Detail(soup)
news_event = utils.Event_Extraction(soup, news_short_desc)
news_summary = utils.Article_Summary(news_text)
total_news_words, news_words = utils.Count_Text_Words(news_text)
total_summary_words, summary_words = utils.Count_Text_Words(
news_summary)
news_subjectivity, news_polarity, news_sentiment, news_complete_nouns, news_cardinal_digit, news_verbs = utils.News_POS(
news_text)
target_names = utils.News_Target_Names(news_summary)
news_classification = utils.News_Classifier(news_text)
country, city, address, latitude, longitude = utils.Geographic_Details(
news_text)
print('**********************************')
print(f'News URL: {url}\n')
print(f'News Source: {name}\n')
print(f'News Section: {news_section}\n')
print(f'News Title: {news_title}\n')
print(f'News Author: {news_authors}\n')
print(
f'News Author Source link: {news_authors_link}\n')
print(f'News Publish Date: {news_date}\n')
print(f'News Publish Time: {news_time}\n')
print(f'News Short Description: {news_short_desc}\n')
print(f'News Image Alt Text: {news_image_text}\n')
print(f'News Top Image URL: {news_image_link}\n')
print(f'News Complete Nouns: {news_complete_nouns}\n')
print(f'News Cardinal Digit: {news_cardinal_digit}\n')
print(
f'News Targeted Persons in News: {target_names}\n')
print(
f'News Total Words in News Details: {total_news_words}\n')
print(
f'News Total Words in News Summary: {total_news_words}\n')
print(f'News Article: {news_text}\n')
print(f'News Summary: {news_summary}\n')
print(
f'News Sentiments WRT Subjectivity: {news_subjectivity}\n')
print(f'News Sentiment Score: {news_polarity}\n')
print(f'News Sentiment Anaylsis: {news_sentiment}\n')
print(f'News Classification: {news_classification}\n')
print(f'News Events: {news_event}\n')
print(f'News Country: {country}\n')
print(f'News City: {city}\n')
print(f'News Address: {address}\n')
print(f'News Latitude: {latitude}\n')
print(f'News Longitude: {longitude}\n')
print('**********************************')
df_news["News_URL"].append(url)
df_news["News_Source"].append(name)
df_news["News_Section"].append(news_section)
df_news["News_Title"].append(news_title)
df_news["News_Date"].append(news_date)
df_news["News_Time"].append(news_time)
df_news["News_Authors"].append(news_authors)
df_news["News_Authors_Source"].append(
news_authors_link)
df_news["News_Image_Link"].append(news_image_link)
df_news["News_Image_Caption"].append(news_image_text)
df_news["News_Short_Description"].append(
news_short_desc)
df_news["News_Proper_Nouns"].append(
news_complete_nouns)
df_news["News_Verbs"].append(news_verbs)
df_news["News_Cardinal_Digit"].append(
news_cardinal_digit)
df_news["News_Target_Names"].append(target_names)
df_news["News_Word_Cloud"].append(news_words)
df_news["News_Summary_Word_Cloud"].append(
summary_words)
df_news["News_Total_Words"].append(total_news_words)
df_news["News_Total_Summary_Words"].append(
total_summary_words)
df_news["News_Detail"].append(news_text)
df_news["News_Summary"].append(news_summary)
df_news["News_Subjectivity"].append(news_subjectivity)
df_news["News_Polarity_Score"].append(news_polarity)
df_news["News_Sentiments"].append(news_sentiment)
df_news["News_Classification"].append(
news_classification)
df_news["News_Event"].append(news_event)
df_news["News_Country"].append(country)
df_news["News_City"].append(city)
df_news["News_Address"].append(address)
df_news["News_Latitude"].append(latitude)
df_news["News_Longitude"].append(longitude)
# except:
# # # print("Oops! authors not found", sys.exc_info()[0], "occurred.")
# continue
df = pd.DataFrame.from_dict(
df_news, orient='index').transpose()
df.to_csv('eurasian_scraped.csv', index=False)
print("Scraping Completed for ", name, " Tab ")
# dff = pd.read_csv('C:/Users/ASDF/Desktop/print_media/dawn_national_scraped.csv')
# df = pd.DataFrame.from_dict(df_news, orient='index').transpose()
# frames = [dff, df]
# result = pd.concat(frames)
# result.to_csv('C:/Users/ASDF/Desktop/print_media/dawn_national_scraped.csv', index=False)
return df_news
def scrap_latest_news(self, latest_url, **kwargs):
print("-------------------------------Scraping Latest Tab News-------------------------------")
article_urls = self.Scrap_Latest_Links(latest_url)
df_latest = self.Scrap_KashmirTimes_News(article_urls, 'KashmirTimes Latest')
# df_latest = pd.DataFrame.from_dict(df_latest)
return df_latest
def scrap_national_news(self, latest_url, **kwargs):
print("-------------------------------Scraping National Tab News-------------------------------")
article_urls = self.Scrap_National_Links(latest_url)
df_national = self.Scrap_KashmirTimes_News(article_urls, 'KashmirTimes India')
# df_national = pd.DataFrame.from_dict(df_national)
return df_national
def scrap_world_news(self, latest_url, **kwargs):
print("-------------------------------Scraping International Tab News-------------------------------")
article_urls = self.Scrap_World_Links(latest_url)
df_world = self.Scrap_KashmirTimes_News(article_urls, 'KashmirTimes International')
# df_national = pd.DataFrame.from_dict(df_news)
return df_world
if __name__ == "__main__":
print("-------------------------------Scraping KashmirTimes News -------------------------------")
scrap = KashmirTimes_News()
latest = 'http://www.kashmirtimes.com/index.aspx'
# national = 'http://www.kashmirtimes.com/news.aspx?q=India'
# world = 'http://www.kashmirtimes.com/news.aspx?q=World'
scrap_latest = scrap.scrap_latest_news(latest)
# scrap_national = scrap.scrap_national_news(national)
# scrap_world = scrap.scrap_world_news(world)
# df_news = pd.DataFrame.from_dict(national)
# df_news.to_csv('all_Eurasian_News_wrold_scraped.csv', index=False)
# df_news.head()