A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)
Dive into the industry and get my hands dirty. That's why I start this self-motivated independent project.
Try this awesome Real-Time Twitter Monitoring System here on Heroku server. Read a series of related articles below:
The solution for evaluating Twitter data to perform better business decisions is to keep tracking all relevant Twitter content about a brand in real-time, perform analysis as topics or issues emerge, and detect anomaly with alert. By monitoring brand mentions on Twitter, brands could inform enagement and deliver better experiences for their customers across the world.
web app has been deployed on Heroku.
Try this interactive data visuilization in Jupyter Notebook. To run with streaming data, you need to deploy it locally.
pip install -r requirements.txt
Create a file called credentials.py
and fill in the following content
# Go to http://apps.twitter.com and create an app.
# The consumer key and secret will be generated for you
API_KEY = "XXXXXXXXXXXXXX"
API_SECRET_KEY = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
# After the step above, you will be redirected to your app's page.
# Create an access token under the the "Your access token" section
ACCESS_TOEKN = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
ACCESS_TOKEN_SECRET = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
Create local MySQL database with info below
host="localhost"
user="root"
passwd="password"
database_table="TwitterDB"
You can change the TRACK_WORDS
in settings.py
into any word, brand, or topic you're interested.
To perform streaming processing on dashboard, you need to deploy all settings above as well as let Main.ipynb
keep listening.
Run Main.ipynb
to start scraping data on Jupter Notebook.
Run Analysis.ipynb
to perform data analysis for brand improvement after Main.ipynb
starts running.
Run Trend_Analysis_Complex
to track topic trends on Twitter after Main.ipynb
starts running.
Note: Since streaming process is always on, press STOP button to finsih.
All things related to Dash App is placed in dash_app
folder.