chat-miner provides lean parsers for every major platform transforming chats into dataframes. Artistic visualizations allow you to explore your data and create artwork from your chats.
Latest release including dependencies can be installed via PyPI:
pip install chat-minerIf you're interested in contributing, running the latest source code, or just like to build everything yourself:
git clone https://github.com/joweich/chat-miner.git
cd chat-miner
pip install -r requirements.txtHave a look at the official tutorials for WhatsApp, Signal, Telegram, Facebook Messenger, or Instagram Chats to learn how to export chat logs for your platform.
Following code showcases the WhatsAppParser module.
The usage of SignalParser, TelegramJsonParser, FacebookMessengerParser, and InstagramJsonParser follows the same pattern.
from chatminer.chatparsers import WhatsAppParser
parser = WhatsAppParser(FILEPATH)
parser.parse_file()
df = parser.parsed_messages.get_df(as_pandas=True) # as_pandas=False returns polars dataframeNote: Depending on your source system, Python requires to convert the filepath to a raw string.
import os
FILEPATH = r"C:\Users\Username\chat.txt" # Windows
FILEPATH = "/home/username/chat.txt" # Unix
assert os.path.isfile(FILEPATH)import chatminer.visualizations as vis
import matplotlib.pyplot as pltfig, ax = plt.subplots(2, 1, figsize=(9, 3))
ax[0] = vis.calendar_heatmap(df, year=2020, cmap='Oranges', ax=ax[0])
ax[1] = vis.calendar_heatmap(df, year=2021, linewidth=0, monthly_border=True, ax=ax[1])
fig, ax = plt.subplots(1, 2, figsize=(7, 3), subplot_kw={'projection': 'polar'})
ax[0] = vis.sunburst(df, highlight_max=True, isolines=[2500, 5000], isolines_relative=False, ax=ax[0])
ax[1] = vis.sunburst(df, highlight_max=False, isolines=[0.5, 1], color='C1', ax=ax[1])
fig, ax = plt.subplots(figsize=(8, 3))
stopwords = ['these', 'are', 'stopwords']
kwargs={"background_color": "white", "width": 800, "height": 300, "max_words": 500}
ax = vis.wordcloud(df, ax=ax, stopwords=stopwords, **kwargs)
if not vis.is_radar_registered():
vis.radar_factory(7, frame="polygon")
fig, ax = plt.subplots(1, 2, figsize=(7, 3), subplot_kw={'projection': 'radar'})
ax[0] = vis.radar(df, ax=ax[0])
ax[1] = vis.radar(df, ax=ax[1], color='C1', alpha=0)
from chatminer.nlp import add_sentiment
df_sentiment = add_sentiment(df)df_grouped = df_sentiment.groupby(['author', 'sentiment']).size().unstack(fill_value=0)
ax = df_grouped.plot(kind='bar', stacked=True, figsize=(8, 3))
The CLI supports parsing chat logs into csv files. As of now, you can't create visualizations from the CLI directly.
Example usage:
$ chatminer -p whatsapp -i exportfile.txt -o output.csvUsage guide:
usage: chatminer [-h] [-p {whatsapp,instagram,facebook,signal,telegram}] [-i INPUT] [-o OUTPUT]
options:
-h, --help
Show this help message and exit
-p {whatsapp,instagram,facebook,signal,telegram}, --parser {whatsapp,instagram,facebook,signal,telegram}
The platform from which the chats are imported
-i INPUT, --input INPUT
Input file to be processed
-o OUTPUT, --output OUTPUT
Output file for the results