IM Logs processing
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