Lemmy_Sentiment1_1752548854

%pip install google-colab-selenium

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Installing collected packages: wsproto, typing_extensions, outcome, trio, trio-websocket, selenium, google-colab-selenium
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[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
torch 2.6.0+cu124 requires nvidia-cublas-cu12==12.4.5.8; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cublas-cu12 12.5.3.2 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cuda-cupti-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-cupti-cu12 12.5.82 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cuda-nvrtc-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-nvrtc-cu12 12.5.82 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cuda-runtime-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cuda-runtime-cu12 12.5.82 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cudnn-cu12==9.1.0.70; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cudnn-cu12 9.3.0.75 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cufft-cu12==11.2.1.3; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cufft-cu12 11.2.3.61 which is incompatible.
torch 2.6.0+cu124 requires nvidia-curand-cu12==10.3.5.147; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-curand-cu12 10.3.6.82 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cusolver-cu12==11.6.1.9; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusolver-cu12 11.6.3.83 which is incompatible.
torch 2.6.0+cu124 requires nvidia-cusparse-cu12==12.3.1.170; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-cusparse-cu12 12.5.1.3 which is incompatible.
torch 2.6.0+cu124 requires nvidia-nvjitlink-cu12==12.4.127; platform_system == "Linux" and platform_machine == "x86_64", but you have nvidia-nvjitlink-cu12 12.5.82 which is incompatible.[0m[31m
[0mSuccessfully installed google-colab-selenium-1.0.14 outcome-1.3.0.post0 selenium-4.33.0 trio-0.30.0 trio-websocket-0.12.2 typing_extensions-4.13.2 wsproto-1.2.0

import google_colab_selenium as gs
from bs4 import BeautifulSoup

import pandas as pd
from bs4 import BeautifulSoup

def extract_comment_data(comments):
    """
    Extracts comment data from the given BeautifulSoup object and returns it as a DataFrame.

    Parameters:
    soup (BeautifulSoup): Parsed HTML content

    Returns:
    DataFrame: A pandas DataFrame containing the comment data.
    """
    # Select all the comment elements
    # comments = soup.select('li.comment.list-unstyled')

    # List to store each comment's data
    comment_data = []

    # Loop through each comment and extract relevant information
    for comment in comments:
        try:
            # Extract author (username)
            author = comment.select_one('a.person-listing span').text.strip()
        except AttributeError:
            author = None

        try:
            # Extract author's profile image
            author_image = comment.select_one('a.person-listing picture img')['src']
        except (AttributeError, TypeError):
            author_image = None

        try:
            # Extract community name and link
            community_name = comment.select_one('a.community-link span').text.strip()
            community_link = comment.select_one('a.community-link')['href']
        except AttributeError:
            community_name, community_link = None, None

        try:
            # Extract post title and link
            post_title = comment.select_one('a.me-2').text.strip()
            post_link = comment.select_one('a.me-2')['href']
        except AttributeError:
            post_title, post_link = None, None

        try:
            # Extract comment content
            comment_text = comment.select_one('div.comment-content div.md-div p').text.strip()
        except AttributeError:
            comment_text = None

        try:
            # Extract timestamp
            timestamp = comment.select_one('span.moment-time')['data-tippy-content']
        except AttributeError:
            timestamp = None

        try:
            # Extract points or score
            points = comment.select_one('span.fw-bold.text-muted').text.strip()
        except AttributeError:
            points = None

        try:
            # Extract language badge if available
            language_badge = comment.select_one('span.badge.text-bg-light').text.strip()
        except AttributeError:
            language_badge = None

        try:
            # Extract comment link (show context button)
            comment_link = comment.select_one('a.btn.btn-link.btn-animate.text-muted')['href']
        except AttributeError:
            comment_link = None

        # Append the extracted data to the list
        comment_data.append({
            'Author': author,
            'Author Image': author_image,
            'Community': community_name,
            'Community Link': community_link,
            'Post Title': post_title,
            'Post Link': post_link,
            'Comment Text': comment_text,
            'Timestamp': timestamp,
            'Points': points,
            'Language Badge': language_badge,
            'Comment Link': comment_link
        })

    # Convert the list of dictionaries to a pandas DataFrame
    df = pd.DataFrame(comment_data)

    return df

user = 'FlyingSquid'

68000/20

3400.0

# 20 comments per page: ~50 pages is the previous 1000 comments.

df_list = []
for i in range(3500):
  driver = gs.Chrome()
  result = driver.get(f'https://lemmy.world/u/{user}?page={i}&sort=New&view=Comments')
  page_source = driver.page_source
  soup = BeautifulSoup(page_source, 'html.parser')
  comments = soup.find_all(class_="comment list-unstyled")
  df = extract_comment_data(comments)
  df_list.append(df)
  driver.quit()

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---------------------------------------------------------------------------

TimeoutError                              Traceback (most recent call last)

/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    533         try:
--> 534             response = conn.getresponse()
    535         except (BaseSSLError, OSError) as e:


/usr/local/lib/python3.11/dist-packages/urllib3/connection.py in getresponse(self)
    515         # Get the response from http.client.HTTPConnection
--> 516         httplib_response = super().getresponse()
    517 


/usr/lib/python3.11/http/client.py in getresponse(self)
   1394             try:
-> 1395                 response.begin()
   1396             except ConnectionError:


/usr/lib/python3.11/http/client.py in begin(self)
    324         while True:
--> 325             version, status, reason = self._read_status()
    326             if status != CONTINUE:


/usr/lib/python3.11/http/client.py in _read_status(self)
    285     def _read_status(self):
--> 286         line = str(self.fp.readline(_MAXLINE + 1), "iso-8859-1")
    287         if len(line) > _MAXLINE:


/usr/lib/python3.11/socket.py in readinto(self, b)
    717             try:
--> 718                 return self._sock.recv_into(b)
    719             except timeout:


TimeoutError: timed out


The above exception was the direct cause of the following exception:


ReadTimeoutError                          Traceback (most recent call last)

<ipython-input-12-18528950> in <cell line: 0>()
      4 for i in range(3500):
      5   driver = gs.Chrome()
----> 6   result = driver.get(f'https://lemmy.world/u/{user}?page={i}&sort=New&view=Comments')
      7   page_source = driver.page_source
      8   soup = BeautifulSoup(page_source, 'html.parser')


/usr/local/lib/python3.11/dist-packages/selenium/webdriver/remote/webdriver.py in get(self, url)
    470         >>> driver.get("https://example.com")
    471         """
--> 472         self.execute(Command.GET, {"url": url})
    473 
    474     @property


/usr/local/lib/python3.11/dist-packages/selenium/webdriver/remote/webdriver.py in execute(self, driver_command, params)
    443                 params["sessionId"] = self.session_id
    444 
--> 445         response = self.command_executor.execute(driver_command, params)
    446         if response:
    447             self.error_handler.check_response(response)


/usr/local/lib/python3.11/dist-packages/selenium/webdriver/remote/remote_connection.py in execute(self, command, params)
    402         trimmed = self._trim_large_entries(params)
    403         LOGGER.debug("%s %s %s", command_info[0], url, str(trimmed))
--> 404         return self._request(command_info[0], url, body=data)
    405 
    406     def _request(self, method, url, body=None):


/usr/local/lib/python3.11/dist-packages/selenium/webdriver/remote/remote_connection.py in _request(self, method, url, body)
    426 
    427         if self._client_config.keep_alive:
--> 428             response = self._conn.request(method, url, body=body, headers=headers, timeout=self._client_config.timeout)
    429             statuscode = response.status
    430         else:


/usr/local/lib/python3.11/dist-packages/urllib3/_request_methods.py in request(self, method, url, body, fields, headers, json, **urlopen_kw)
    141             )
    142         else:
--> 143             return self.request_encode_body(
    144                 method, url, fields=fields, headers=headers, **urlopen_kw
    145             )


/usr/local/lib/python3.11/dist-packages/urllib3/_request_methods.py in request_encode_body(self, method, url, fields, headers, encode_multipart, multipart_boundary, **urlopen_kw)
    276         extra_kw.update(urlopen_kw)
    277 
--> 278         return self.urlopen(method, url, **extra_kw)


/usr/local/lib/python3.11/dist-packages/urllib3/poolmanager.py in urlopen(self, method, url, redirect, **kw)
    441             response = conn.urlopen(method, url, **kw)
    442         else:
--> 443             response = conn.urlopen(method, u.request_uri, **kw)
    444 
    445         redirect_location = redirect and response.get_redirect_location()


/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    839                 new_e = ProtocolError("Connection aborted.", new_e)
    840 
--> 841             retries = retries.increment(
    842                 method, url, error=new_e, _pool=self, _stacktrace=sys.exc_info()[2]
    843             )


/usr/local/lib/python3.11/dist-packages/urllib3/util/retry.py in increment(self, method, url, response, error, _pool, _stacktrace)
    472             # Read retry?
    473             if read is False or method is None or not self._is_method_retryable(method):
--> 474                 raise reraise(type(error), error, _stacktrace)
    475             elif read is not None:
    476                 read -= 1


/usr/local/lib/python3.11/dist-packages/urllib3/util/util.py in reraise(tp, value, tb)
     37         if value.__traceback__ is not tb:
     38             raise value.with_traceback(tb)
---> 39         raise value
     40     finally:
     41         value = None  # type: ignore[assignment]


/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)
    785 
    786             # Make the request on the HTTPConnection object
--> 787             response = self._make_request(
    788                 conn,
    789                 method,


/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)
    534             response = conn.getresponse()
    535         except (BaseSSLError, OSError) as e:
--> 536             self._raise_timeout(err=e, url=url, timeout_value=read_timeout)
    537             raise
    538 


/usr/local/lib/python3.11/dist-packages/urllib3/connectionpool.py in _raise_timeout(self, err, url, timeout_value)
    365 
    366         if isinstance(err, SocketTimeout):
--> 367             raise ReadTimeoutError(
    368                 self, url, f"Read timed out. (read timeout={timeout_value})"
    369             ) from err


ReadTimeoutError: HTTPConnectionPool(host='localhost', port=36171): Read timed out. (read timeout=120)

df = pd.concat(df_list)

df

<div>

	Author	Author Image	Community	Community Link	Post Title	Post Link	Comment Text	Timestamp	Points	Language Badge	Comment Link
0	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Trump deletes nationwide database on police mi…	/post/25908134	Why does anyone think the squiggly red line si…	Saturday, February 22nd, 2025 at 10:49:34 AM G…	None	None	/post/25908134/15246638
1	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	World News	/c/world	91% of Ukrainians oppose peace talks without U…	/post/25912036	Plus, there are contrarians everywhere.	Saturday, February 22nd, 2025 at 10:48:46 AM G…	None	English	/post/25912036/15246886
2	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	TenForward: Where Every Vulcan Knows Your Name	/c/tenforward	lcarsde - LCARS Desktop Environment	/post/25903377	Fair enough. I was suggesting that Michael Oku…	Created: Saturday, February 22nd, 2025 at 9:53…	None	None	/post/25903377/15246407
3	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	politics	/c/politics	Former intelligence officer claims KGB recruit…	/post/25880762	He’s not pretending.	Saturday, February 22nd, 2025 at 9:52:31 AM GM…	None	None	/post/25880762/15245859
4	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Microblog Memes	/c/microblogmemes	it keeps happening	/post/25892731	It was enough of a Nazi salute for the head of…	Saturday, February 22nd, 2025 at 8:46:26 AM GM…	None	English	/post/25892731/15241060
…	…	…	…	…	…	…	…	…	…	…	…
15	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Putin could be open to a pause in fighting ami…	/post/9916653	Please answer the question.	Sunday, December 24th, 2023 at 5:27:56 PM GMT+…	None	None	/post/9916653/6224294
16	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Political Memes	/c/politicalmemes	Red State Public School in 2024	/post/9932785	I bet they leave in plenty of time to talk abo…	Sunday, December 24th, 2023 at 5:14:23 PM GMT+…	None	None	/post/9932785/6224136
17	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Lemmy Shitpost	/c/lemmyshitpost	Pov: You don’t agree with la lemmy hivemind.	/post/9933447	How do I join this hive mind exactly? I’ve nev…	Sunday, December 24th, 2023 at 5:10:39 PM GMT+…	None	None	/post/9933447/6224123
18	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Political Memes	/c/politicalmemes	Red State Public School in 2024	/post/9932785	I always think of that line in the movie The R…	Sunday, December 24th, 2023 at 5:07:40 PM GMT+…	None	None	/post/9932785/6223858
19	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Putin could be open to a pause in fighting ami…	/post/9916653	You said:	Sunday, December 24th, 2023 at 5:00:56 PM GMT+…	None	None	/post/9916653/6223967

51600 rows × 11 columns

<div class="colab-df-buttons">

<button class="colab-df-convert" onclick="convertToInteractive('df-41dba481-3f69-4a85-b095-6c12b9cf9ad3')"
        title="Convert this dataframe to an interactive table."
        style="display:none;">

<script>
  const buttonEl =
    document.querySelector('#df-41dba481-3f69-4a85-b095-6c12b9cf9ad3 button.colab-df-convert');
  buttonEl.style.display =
    google.colab.kernel.accessAllowed ? 'block' : 'none';

  async function convertToInteractive(key) {
    const element = document.querySelector('#df-41dba481-3f69-4a85-b095-6c12b9cf9ad3');
    const dataTable =
      await google.colab.kernel.invokeFunction('convertToInteractive',
                                                [key], {});
    if (!dataTable) return;

    const docLinkHtml = 'Like what you see? Visit the ' +
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df.to_csv('comments.csv')

import pandas as pd
df = pd.read_csv('comments.csv')

from datetime import datetime
import re

# Function to clean and extract the timestamp part and remove day suffix
def clean_timestamp(text):
    # Extract the timestamp part using regex
    match = re.search(r'\w+day, \w+ \d{1,2}\w{2}, \d{4} at \d{1,2}:\d{2}:\d{2} [APM]+ GMT[\+\-]\d{2}:\d{2}', text)
    if match:
        # Remove the day suffix (st, nd, rd, th) using regex
        cleaned_text = re.sub(r'(\d{1,2})(st|nd|rd|th)', r'\1', match.group(0))
        return cleaned_text
    else:
        return None

# Define the correct format string (after removing the day suffix)
format_str = "%A, %B %d, %Y at %I:%M:%S %p GMT%z"

# Apply the extraction, cleaning, and parsing
df['Parsed_Timestamp'] = df['Timestamp'].apply(lambda x: datetime.strptime(clean_timestamp(str(x)), format_str) if clean_timestamp(str(x)) else None)

# Display the DataFrame with parsed timestamps
print(df[['Timestamp', 'Parsed_Timestamp']])

                                               Timestamp  \
0      Saturday, February 22nd, 2025 at 10:49:34 AM G...   
1      Saturday, February 22nd, 2025 at 10:48:46 AM G...   
2      Created: Saturday, February 22nd, 2025 at 9:53...   
3      Saturday, February 22nd, 2025 at 9:52:31 AM GM...   
4      Saturday, February 22nd, 2025 at 8:46:26 AM GM...   
...                                                  ...   
51595  Sunday, December 24th, 2023 at 5:27:56 PM GMT+...   
51596  Sunday, December 24th, 2023 at 5:14:23 PM GMT+...   
51597  Sunday, December 24th, 2023 at 5:10:39 PM GMT+...   
51598  Sunday, December 24th, 2023 at 5:07:40 PM GMT+...   
51599  Sunday, December 24th, 2023 at 5:00:56 PM GMT+...   

               Parsed_Timestamp  
0     2025-02-22 10:49:34+00:00  
1     2025-02-22 10:48:46+00:00  
2     2025-02-22 09:53:21+00:00  
3     2025-02-22 09:52:31+00:00  
4     2025-02-22 08:46:26+00:00  
...                         ...  
51595 2023-12-24 17:27:56+00:00  
51596 2023-12-24 17:14:23+00:00  
51597 2023-12-24 17:10:39+00:00  
51598 2023-12-24 17:07:40+00:00  
51599 2023-12-24 17:00:56+00:00  

[51600 rows x 2 columns]

import pandas as pd
import torch
# from datasets import load_dataset
from transformers import pipeline
from sklearn.metrics import matthews_corrcoef

device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = pipeline("zero-shot-classification", model='mlburnham/Political_DEBATE_base_v1.0', device = device, batch_size = 32) # To use the base model

config.json:   0%|          | 0.00/1.07k [00:00<?, ?B/s]



model.safetensors:   0%|          | 0.00/738M [00:00<?, ?B/s]



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special_tokens_map.json:   0%|          | 0.00/1.02k [00:00<?, ?B/s]


Device set to use cuda

samples = [str(i) for i in df['Comment Text']]

samples = [' ' if len(samp) == 0 else samp for samp in samples]

# Thesis:
template = 'The author of this text {} .'

# Set up hypothesis test:
labels = ['enaging in respectful, normal internet banter', 'is trolling or otherwise being rude.']

# classify the documents
# The multilabel option determines if more than one hypothesis can be true for the document.
# If false, the most likely label is returned. If true, a dictionary of labels and their estimated probability is returned.
# for i in range(len(samples)):
res = pipe(samples , labels, hypothesis_template = template, multi_label = False)

# Creating a DataFrame for each result and concatenating them
sent = pd.concat([pd.DataFrame([i["scores"]], columns=i["labels"]) for i in res], ignore_index=True)

# Resetting index of both DataFrames before concatenating
df = df.reset_index(drop=True)
sent = sent.reset_index(drop=True)

# merged_df = pd.concat([df, sent], axis=1)
merged_df = pd.concat([df, sent], axis=1)

from transformers import pipeline
import pandas as pd

# 1️⃣ Prepare your texts
samples = [str(i) for i in df['Comment Text']]
samples = [' ' if not s else s for s in samples]

# 2️⃣ Set up zero‐shot classifier with BART‐MNLI
classifier = pipeline(
    "zero-shot-classification",
    model="facebook/bart-large-mnli",
    hypothesis_template="This text {}."
)

# 3️⃣ Define candidate labels
candidate_labels = ["mentions location", "does not mention location"]

# 4️⃣ Classify
results = classifier(samples, candidate_labels, multi_label=False)

# 5️⃣ Parse out label, score, and boolean flag
labels = [r["labels"][0] for r in results]
scores = [r["scores"][0] for r in results]
mentions = [lbl == "mentions location" for lbl in labels]

# 6️⃣ Build DataFrame and merge
nli_df = pd.DataFrame({
    "nli_label": labels,
    "nli_score": scores,
    "mentions_location": mentions
})
merged_df = pd.concat([df.reset_index(drop=True), nli_df], axis=1)

config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]



model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]



tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]



vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]



merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]



tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]


Device set to use cuda:0

merged_df

<div>

	Unnamed: 0	Author	Author Image	Community	Community Link	Post Title	Post Link	Comment Text	Timestamp	Points	Language Badge	Comment Link	Parsed_Timestamp	Hour	YearMonth	YearWeek	DeltaMin	mentions_location	location_mentioned
0	14	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	[email protected]	/c/[email protected]	Me when I see Jonathan Frakes directed an episode	/post/9932367	Not sure if he still is, but he used to be kno…	Sunday, December 24th, 2023 at 5:38:32 PM GMT+…	NaN	English	/post/9932367/6224456	2023-12-24 17:38:32+00:00	17	2023-12	2023-12-18/2023-12-24	10.600000	False	None
1	13	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Putin could be open to a pause in fighting ami…	/post/9916653	Now you’re not actually mentally handicapped a…	Sunday, December 24th, 2023 at 5:41:07 PM GMT+…	NaN	NaN	/post/9916653/6224446	2023-12-24 17:41:07+00:00	17	2023-12	2023-12-18/2023-12-24	2.583333	False	None
2	12	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Inside the MAGA Plan to Attack Birth Control, …	/post/9932713	Oh they probably are, but this is like stuff p…	Sunday, December 24th, 2023 at 5:48:11 PM GMT+…	NaN	NaN	/post/9932713/6224567	2023-12-24 17:48:11+00:00	17	2023-12	2023-12-18/2023-12-24	7.066667	False	None
3	11	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Judge suggests change to nitrogen execution to…	/post/9862877	Right, but for them, it’s all about revenge an…	Sunday, December 24th, 2023 at 5:51:27 PM GMT+…	NaN	NaN	/post/9862877/6224546	2023-12-24 17:51:27+00:00	17	2023-12	2023-12-18/2023-12-24	3.266667	False	None
4	10	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Political Memes	/c/politicalmemes	Red State Public School in 2024	/post/9932785	I’m pretty sure the cartoon is not about kids …	Sunday, December 24th, 2023 at 5:53:14 PM GMT+…	NaN	NaN	/post/9932785/6224643	2023-12-24 17:53:14+00:00	17	2023-12	2023-12-18/2023-12-24	1.783333	False	None
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
51590	4	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	Microblog Memes	/c/microblogmemes	it keeps happening	/post/25892731	It was enough of a Nazi salute for the head of…	Saturday, February 22nd, 2025 at 8:46:26 AM GM…	NaN	English	/post/25892731/15241060	2025-02-22 08:46:26+00:00	8	2025-02	2025-02-17/2025-02-23	3.316667	False	None
51591	3	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	politics	/c/politics	Former intelligence officer claims KGB recruit…	/post/25880762	He’s not pretending.	Saturday, February 22nd, 2025 at 9:52:31 AM GM…	NaN	NaN	/post/25880762/15245859	2025-02-22 09:52:31+00:00	9	2025-02	2025-02-17/2025-02-23	66.083333	False	None
51592	2	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	TenForward: Where Every Vulcan Knows Your Name	/c/tenforward	lcarsde - LCARS Desktop Environment	/post/25903377	Fair enough. I was suggesting that Michael Oku…	Created: Saturday, February 22nd, 2025 at 9:53…	NaN	NaN	/post/25903377/15246407	2025-02-22 09:53:21+00:00	9	2025-02	2025-02-17/2025-02-23	0.833333	False	None
51593	1	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	World News	/c/world	91% of Ukrainians oppose peace talks without U…	/post/25912036	Plus, there are contrarians everywhere.	Saturday, February 22nd, 2025 at 10:48:46 AM G…	NaN	English	/post/25912036/15246886	2025-02-22 10:48:46+00:00	10	2025-02	2025-02-17/2025-02-23	55.416667	False	None
51594	0	Flying Squid	https://lemmy.world/pictrs/image/dbaaada6-12c3…	News	/c/news	Trump deletes nationwide database on police mi…	/post/25908134	Why does anyone think the squiggly red line si…	Saturday, February 22nd, 2025 at 10:49:34 AM G…	NaN	NaN	/post/25908134/15246638	2025-02-22 10:49:34+00:00	10	2025-02	2025-02-17/2025-02-23	0.800000	False	None

51595 rows × 19 columns

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[print(i) for i in merged_df[merged_df['mentions_location'] ==True]['location_mentioned'].tail(100)]

that probably didn’t include rape threats
Elsewhere in this thread I described the Cybertruck
we’re not in that timeline
they don’t care
we’re not talking about a Kalevala
they just get released on bond rather than taken away from their family and imprisoned
You didn’t actually provide the comments
That is not what people are discussing
it’s impossible to tell at this point.
insinuating that he wasn’t aware
plenty people here know the Circle Jerks
it doesn’t even matter to them when I tell them
A couple of illegal immigrants
the Nazis or the Italians
none of them
You don’t need to remind me
Greenland
there was not a single mention of North Korea
before slaves were emancipated and women were given rights.”
Mr. “We Must Colonize Mars
We had them in WWII
Andy’s friend Bob Zmuda was also Clifton
If they didn’t explicitly do it every single times
a trans girl is on a middle school girl’s volleyball team
she “passes” enough to give OP a funny feeling downstairs
pro-capitalist views are noted
horse-drawn papal carriages
the Europeans specifically in Ukraine
My family is Jewish
I can’t find you something offhand
Jewish orphans
the nearest cemetery
If you lived in a shithole one-room apartment in Bangalore
it’s right at the base of an outer wall
I can’t speak for them
they did have the power
A lot of the people here are not native English speakers
as long as it’s obvious
your insults are noted
she would absolutely suggest that other women deserved it
Transylvania
I have already noted it
every kid in America
where you live
I am doubtful.
no one’s answered
I was talking to the troll themselves
the brave revolutionary runs away when asked to clarify
Dubai
They don’t have to call her
they lived in Japan
They said nothing
I know they didn’t think it through
I don’t think they did understand it
my wife is still in the U.S.
same-sex couple
it turns out it was an Afghani immigrant
one of them talked about it
yes it did.
I haven’t forgotten
I bet they don’t.
when an AI says it, those same people think it’s authoritative
you have no idea what they can do.
If someone makes an unclear demand
I never said anything about disarming
he has no legal right to it
Central Valley and northern Californian populations
Showing with both names
North Korea has eliminated sexual discrimination!”
I did not say in China
Nor does the person who made that video.
they never have.
You did not address what I said at all
he didn’t do that either
“it wouldn’t have worked anyway.”
They are Jewish
anyone builds their identity around this community
I didn’t say that
Vance is far from the only one doing this.
Best to check these things before saying them
They didn’t tell us the bad stories
I don’t know where you live, nor do I care
none of that has anything to do with my point about the article
the kid is right next to her
York, UK
Charles Dickens was raised in the U.S.
some other random guy says it and you believe it
These are people who own multiple mansions
the people dumb enough to drive in NYC
I have walked all over the room in bare feet
I doubt they bothered looking
it’s not true
Whether they had the knowledge
where your bathing suit covers
everyone knows you
I don’t know
my much older brother’s college roommate and eventual best friend
One person talks about a trans agenda
I can’t speak for them
It is not the topic of this post





[None,
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[print(i) for i in  merged_df.sort_values(by=labels[0],ascending=False).iloc[0:int(len(merged_df)*.2)].sort_values(by=labels[0],ascending=False).iloc[0:5]['Comment Text']]

It’s a great forum. Everyone is friendly.
Your transparency, as always, is appreciated!
I think, again, that if you introduce a deadly pathogen to a bear population from a foreign bear, you kill a lot more bears in much more painful ways than shooting them. The article discusses the pathogen issue.
They thank the pallbearers for their support.
I agree 100%. Star Trek helped teach me about diversity and equality and respect for other cultures. Also the concept of talking things out before guns start going off.





[None, None, None, None, None]

# merged_df

# merged_df.sort_values(by='specifically mentions or discusses politics',ascending=False).iloc[0:int(len(merged_df)*.2)]

import pandas as pd
import matplotlib.pyplot as plt


# Convert the 'Parsed_Timestamp' column to datetime in the merged_df
merged_df['Parsed_Timestamp'] = pd.to_datetime(merged_df['Parsed_Timestamp'], errors='coerce')

# Drop NaT if any
merged_df = merged_df.dropna(subset=['Parsed_Timestamp'])

# Group by week
merged_df['Week'] = merged_df['Parsed_Timestamp'].dt.to_period('W')

# Create a new DataFrame with only the relevant columns
columns_of_interest = labels

# Find the column with the max value for each row (you'll need to ensure that your dataset is structured appropriately)
merged_df['Max_Category'] = merged_df[columns_of_interest].idxmax(axis=1)

# Calculate the percentage by week for each category
weekly_percentage = merged_df.sort_values(by=labels[0],ascending=False).iloc[0:int(len(merged_df)*.2)].groupby('Week')['Max_Category'].value_counts(normalize=True).unstack().fillna(0) * 100

# Plot the weekly percentage
plt.figure(figsize=(10,6))
weekly_percentage.plot(kind='line', marker='o', ax=plt.gca())

# Title and labels
plt.title(f'Weekly Percentage for {user}: {template}')
plt.xlabel('Week')
plt.ylabel('Percentage (%)')
plt.xticks(rotation=45)
# plt.legend(columns_of_interest, title='Category')
plt.tight_layout()

# Show the plot
plt.show()

<ipython-input-24-2054246187>:12: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  merged_df['Week'] = merged_df['Parsed_Timestamp'].dt.to_period('W')

unique_hours = sorted(df['Parsed_Timestamp'].dt.hour.unique())
print(unique_hours)

[np.int32(0), np.int32(1), np.int32(2), np.int32(3), np.int32(4), np.int32(5), np.int32(6), np.int32(7), np.int32(8), np.int32(9), np.int32(10), np.int32(11), np.int32(12), np.int32(13), np.int32(14), np.int32(15), np.int32(16), np.int32(17), np.int32(18), np.int32(19), np.int32(20), np.int32(21), np.int32(22), np.int32(23)]

import pandas as pd
import matplotlib.pyplot as plt

# Extract hours
df['Hour'] = df['Parsed_Timestamp'].dt.hour

# Plot histogram
plt.figure(figsize=(10,6))
plt.hist(df['Hour'], bins=24, range=(0, 24), color='lightcoral', edgecolor='black', align='left')
plt.title('Histogram of Comments by Hour')
plt.xlabel('Hour of Day')
plt.ylabel('Count')
plt.xticks(range(24))  # Ensure all hours 0-23 are labeled
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# ensure timestamp is datetime
df['Parsed_Timestamp'] = pd.to_datetime(df['Parsed_Timestamp'])

# extract Hour and YearMonth
df['Hour'] = df['Parsed_Timestamp'].dt.hour
df['YearMonth'] = df['Parsed_Timestamp'].dt.to_period('M')

# count comments per month-hour and compute days in month
counts = (
    df
    .groupby(['YearMonth','Hour'])
    .size()
    .reset_index(name='count')
)
counts['days_in_month'] = counts['YearMonth'].apply(lambda p: p.days_in_month)
counts['avg_comments_per_day'] = counts['count'] / counts['days_in_month']

# plot for each month
for period in sorted(counts['YearMonth'].unique()):
    monthly = counts[counts['YearMonth'] == period]
    avg_by_hour = np.zeros(24)
    for _, row in monthly.iterrows():
        avg_by_hour[int(row['Hour'])] = row['avg_comments_per_day']
    plt.figure(figsize=(10, 6))
    plt.bar(np.arange(24), avg_by_hour, edgecolor='black', align='center')
    plt.title(f'Average Comments per Hour per Day — {period}')
    plt.xlabel('Hour of Day')
    plt.ylabel('Avg Comments per Day')
    plt.xticks(range(24))
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.tight_layout()
    plt.show()

<ipython-input-28-4243116769>:10: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df['YearMonth'] = df['Parsed_Timestamp'].dt.to_period('M')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# ensure timestamp is datetime
df['Parsed_Timestamp'] = pd.to_datetime(df['Parsed_Timestamp'])

# extract Hour and ISO week
df['Hour'] = df['Parsed_Timestamp'].dt.hour
df['YearWeek'] = df['Parsed_Timestamp'].dt.to_period('W')

# count comments per week-hour
counts = (
    df
    .groupby(['YearWeek', 'Hour'])
    .size()
    .reset_index(name='count')
)

# compute number of days with data in each week
days = (
    df
    .groupby('YearWeek')['Parsed_Timestamp']
    .apply(lambda x: x.dt.date.nunique())
    .reset_index(name='days_in_week')
)

# merge and compute average per day
counts = counts.merge(days, on='YearWeek')
counts['avg_comments_per_day'] = counts['count'] / counts['days_in_week']

# plot for each week
for week in sorted(counts['YearWeek'].unique()):
    weekly = counts[counts['YearWeek'] == week]
    avg_by_hour = np.zeros(24)
    for _, row in weekly.iterrows():
        avg_by_hour[int(row['Hour'])] = row['avg_comments_per_day']

    plt.figure(figsize=(10, 6))
    plt.bar(np.arange(24), avg_by_hour, edgecolor='black', align='center')
    plt.title(f'Average Comments per Hour per Day — Week {week}')
    plt.xlabel('Hour of Day')
    plt.ylabel('Avg Comments per Day')
    plt.xticks(range(24))
    plt.grid(axis='y', linestyle='--', alpha=0.7)
    plt.tight_layout()
    plt.show()

<ipython-input-29-2013787792>:10: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df['YearWeek'] = df['Parsed_Timestamp'].dt.to_period('W')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter

# ensure timestamp is datetime and sort
df['Parsed_Timestamp'] = pd.to_datetime(df['Parsed_Timestamp'])
df = df.sort_values('Parsed_Timestamp')

# compute inter‐comment interval in minutes
df['DeltaMin'] = df['Parsed_Timestamp'].diff().dt.total_seconds() / 60
df = df.dropna(subset=['DeltaMin'])

# extract hour and ISO week
df['Hour'] = df['Parsed_Timestamp'].dt.hour
df['YearWeek'] = df['Parsed_Timestamp'].dt.to_period('W')

# average delta per week/hour
avg_delta = (
    df
    .groupby(['YearWeek', 'Hour'])['DeltaMin']
    .mean()
    .reset_index()
)

# plot for each week as bar chart with semi-log y-axis, plain ticks 1–10
for week in sorted(avg_delta['YearWeek'].unique()):
    weekly = avg_delta[avg_delta['YearWeek'] == week]
    bar_vals = weekly.set_index('Hour')['DeltaMin'].reindex(range(24), fill_value=0)

    fig, ax = plt.subplots(figsize=(10, 6))
    ax.bar(bar_vals.index, bar_vals.values, edgecolor='black', align='center')
    ax.set_yscale('symlog', linthresh=10)
    ax.yaxis.set_major_formatter(ScalarFormatter())
    ax.ticklabel_format(style='plain', axis='y')
    ax.set_yticks(range(1, 11))
    ax.set_xticks(range(24))

    ax.set_title(f'Avg Time Between Comments (min) per Hour — Week {week}')
    ax.set_xlabel('Hour of Day')
    ax.set_ylabel('Average Interval Between Comments (minutes)')
    ax.grid(axis='y', linestyle='--', alpha=0.7)
    fig.tight_layout()
    plt.show()

<ipython-input-34-584942059>:16: UserWarning: Converting to PeriodArray/Index representation will drop timezone information.
  df['YearWeek'] = df['Parsed_Timestamp'].dt.to_period('W')