chat-miner: turn your chats into artwork

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.

1. Installation

Latest release including dependencies can be installed via PyPI:

pip install chat-miner

If 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.txt

2. 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(as_pandas=True) # as_pandas=False returns polars dataframe

Note: 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)

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(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)

5. Natural Language Processing

5.1 Add Sentiment

from chatminer.nlp import add_sentiment

df_sentiment = add_sentiment(df)

5.2 Example Plot: Sentiment per Author in Groupchat

df_grouped = df_sentiment.groupby(['author', 'sentiment']).size().unstack(fill_value=0)
ax = df_grouped.plot(kind='bar', stacked=True, figsize=(8, 3))

6. Command Line Interface

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.csv

Usage 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