For the past three years, I have backed up all my IM logs. At the time, I didn't really know why. I thought that maybe one day I would want to read something again. Well, my logs have 150Mb now and I probably won't be reading through it any time soon.
But this year I did two courses on Coursera, Machine Learning and Natural Language Processing, that started to make me think. Maybe I could build some tools to help me analyze my logs and process some meaningfull information out of them. What information is that? I don't know yet. But it's a work in progress.
- Process the logs of various IM programs
- Process Digsby logs
- Process Trillian logs
- Process Pidgin logs
- Process Whatsapp emailed chats
- Process Facebook takeout data
- Process Hangouts takeout data
- Store the logs as efficiently as possible
- Make pretty graphs out of evolution of most popular contacts
- Most common words
- Figure out clusters in my contacts
- To infinity and beyond
Facebook messages from the takeout data should be prettified. The HTML output is more consistent then and easier to parse.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org