Datasets:
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
Tags:
natural-language-understanding
ideology classification
text classification
natural language processing
License:
File size: 4,317 Bytes
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"""This script is used to get many posts from the desired subreddit(s)"""
import requests
import time
import random
import copy
import pandas as pd
"""This script is used to get many posts from the desired subreddit(s)"""
subreddit_list = [
"theredpillrebooted",
"RedPillWomen",
"Feminism",
"marriedredpill",
"TheBluePill",
"PurplePillDebate",
"RedPillWives",
"askMRP",
"ForeverAloneWomen",
]
url_template = "https://www.reddit.com/r/{}/.json?t=all{}"
headers = {"User-Agent": "Testing Bot Gundam Wing"}
params = ""
str_log = []
original_counter = 10000
counter = original_counter
post_list = []
for subreddit in subreddit_list:
while counter > 0:
print(f"Getting posts with params: {params}")
print("\n\n\n\n")
url = url_template.format(subreddit, params)
response = requests.get(url, headers=headers)
if response.ok:
data = response.json()
posts = data["data"]["children"]
print(f"Got {len(posts)} posts")
for post in posts:
pdata = post["data"]
post_id = pdata["id"]
title = pdata["title"]
text = pdata.get("selftext")
score = pdata["score"]
author = pdata["author"]
date = pdata["created_utc"]
url = pdata.get("url_overridden_by_dest")
print(f"{post_id}: {title} - {url}")
# prints for debugging
# print("Keys are ", pdata.keys())
# post_list.append(
# {
# "id": post_id,
# "title": title,
# "text": text,
# "url": url,
# "score": score,
# "author": author,
# "date": date,
# "pdata": pdata,
# }
# )
post_list.append(
[subreddit, post_id, title, text, url, score, author, date, pdata]
)
print(f"Got {len(posts)} posts")
try:
params = "&after=" + data["data"]["after"]
except:
print(
"No more posts, broke on ", subreddit, "with counter at ", counter
)
# write this log to a txt file
str_log.append(
"No more posts, broke on "
+ subreddit
+ "with counter at "
+ str(counter)
)
break
counter -= 1
time.sleep(random.randint(1, 45))
else:
print(f"Error: {response.status_code}")
counter = original_counter
params = ""
# make a copy of the list
post_list_copy = copy.deepcopy(post_list)
# save the list to a csv file, as a backup
# to avoid running the script again
df = pd.DataFrame(post_list_copy)
df.columns = [
"subreddit",
"id",
"title",
"text",
"url",
"score",
"author",
"date",
"pdata",
]
df.to_csv("reddit_posts.csv", index=False)
# Add useful features to the dataframe
def pull_info_from_reddit_dict(
dict,
fields=[
"subreddit_subscribers",
"num_comments",
"ups",
"downs",
"upvote_ratio",
"is_video",
],
):
"""This function takes a dictionary from the Reddit API and returns a list of the values for the fields specified"""
return [dict.get(field, "Not Found") for field in fields]
# Create a new lists of posts with the additional fields
processed_posts = []
for post in post_list_copy:
fields_to_add = pull_info_from_reddit_dict(post[8])
temp_post = post[:-1] + fields_to_add
processed_posts.append(temp_post)
# save the final csv
df = pd.DataFrame(
processed_posts,
columns=[
"subreddit",
"id",
"title",
"text",
"url",
"score",
# "author", author is not useful for the analysis
"date",
"subreddit_subscribers",
"num_comments",
"ups",
"downs",
"upvote_ratio",
"is_video",
],
)
df.to_csv("reddit_posts_fm.csv", index=False)
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