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
bysource ./bin/activate
, install any dependencies bypip 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 torequirements.txt
-
deactivate your
venv
bydeactivate
, 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 avenv
-
activate your
venv
and runpip 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.