chat-miner: turn your chats into artwork
chat-miner provides lean parsers for every major platform transforming chats into pandas dataframes. Artistic visualizations allow you to explore your data and create artwork from your chats.
1. Installation
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.txt2. Exporting chat logs
Have 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.
3. Parsing
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()4. Visualizing
import chatminer.visualizations as vis
import matplotlib.pyplot as plt4.1 Heatmap: Message count per day
fig, 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])
4.2 Sunburst: Message count per daytime
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])
4.3 Wordcloud: Word frequencies
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)
4.4 Radarchart: Message count per weekday
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)
