chat-miner 0.1.0

Lean parsers and visualizations for chat data

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 differently and create artwork from your chats.

1. Installation

git clone https://github.com/joweich/chat-miner.git
cd chat-miner
pip install -r requirements.txt

2. Exporting chat logs

Have a look at the official tutorials for WhatsApp, Signal, Telegram, or Facebook Messenger to learn how to export chat logs for your platform.

3. Parsing

Following code showcases the WhatsAppParser module. The usage of SignalParser, TelegramJsonParser, and FacebookMessengerParser follows the same pattern.

from chatminer.chatparsers import WhatsAppParser

parser = WhatsAppParser(FILEPATH)
parser.parse_file_into_df()

4. Visualizing

import chatminer.visualizations as vis
import matplotlib.pyplot as plt

4.1 Heatmap: Message count per day

fig, ax = plt.subplots(2, 1, figsize=(9, 3))
ax[0] = vis.calendar_heatmap(parser.df, year=2020, cmap='Oranges', ax=ax[0])
ax[1] = vis.calendar_heatmap(parser.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(parser.df, highlight_max=True, isolines=[2500, 5000], isolines_relative=False, ax=ax[0])
ax[1] = vis.sunburst(parser.df, highlight_max=False, isolines=[0.5, 1], color='C1', ax=ax[1])

4.3 Wordcloud: Visualize 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(parser.df, ax=ax, stopwords=stopwords, **kwargs)