Stock sentiment analysis project, correlate stock behaviors and tweet sentiments.

This project only provides a single node deployment method guidance using docker-compose, users may also use bootstrap script to customize their own builds in cluster environments.

make sure you install Docker (version >= 18) and run the following command to bootstrap Sentimento in your computer:

docker-compose up

System: Mac OSX (10.12.6) Sierra or higher
Storage: 16GB RAM, 100GB+ Disk space

Note: This project requires the machine to have at least 16GB of RAM and more than 100GB of disk storage to fully operate. If not so, the data is insufficient to provide a more accurate result.

Make sure for each module, use the specific Python version noted in .python-version

Follow the instructions on this site to install pip and virtualenv

Then, to start a new module development:

• cd to the module directory

• run virtualenv venv to create a new isolated environment

• activate your venv by source ./bin/activate, install any dependencies by pip install <your dependency>

• The directory of venv contains all libraries and binaries you will use under your module and it is not check into the source.

• before deactivation, run pip freeze --local > requirements.txt to dump module dependencies to requirements.txt

• deactivate your venv by deactivate, specify a .python-version file with your module's Python version

then you are done.

To work with existing modules:

• cd to the module directory

• install a Python version specified in .python-version

• run virtualenv venv to create a venv

• activate your venv and run pip install -r requirements.txt to install dependencies for that module

• deactivate as above

In Zeppelin dashboard, an example of a stock price movement versus the average sentiment values of relevant tweets, Facebook (symbol: FB) share has relative higher price with more positive sentiments while lower price with more negative sentiments.

Copyright © 2018, Sentimento is licensed under MIT.