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:
- Chapter 1: Collecting Twitter Data using Streaming Twitter API with Tweepy, MySQL, & Python
- Chapter 2: Twitter Sentiment Analysis and Interactive Data Visualization using RE, TextBlob, NLTK, and Plotly
- Chapter 3: Deploy a Real-time Twitter Analytical Web App on Heroku using Dash & Plotly in Python
- Chapter 4 (In Progress): Parallelize Streaming Twitter Sentiment Analysis using Scala, Kafka and Spark Streaming
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.
- Less 0.01% users will push tweets with their locations.
- Tweets grabbed from streaming data won't have more than 0 LIKE or RETWEET, since you have already captured them even before others press buttons :p
- More than 65.6% users will write the locations in their profile, although very few of them don't live on Earth according to that fact.
- The numbers of positive and negative tweets are relatively close and stay low compared with neural tweet number. Unless emergency events happen, lines won't fluctuate acutely.
- Build ETL pipelines based on stream processing using Kafka (In Progress)
- Perform sentiment analysis using Spark Streaming (In Progress)
- Extract streaming Twitter Data, preprocess data in Python, and load data into MySQL for storage
- Perform exploratory data analysis with Pandas & Seaborn to explore the insights
- Connect with Plotly for real-time interactive dashboard based on time series
- Deploy the real-time interactive front-end web app using Dash & Heroku PostgreSQL on Heroku server
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.
- Unstructured tweet texts may contain messy code and emoji characters
- Some brands may take a long time to collection enough data to perform analysis on issue emerging since they target specify groups of people
- Some hot words will uncover useful insights only after appearing more than 10k times on tweets
- Plotly doesn't have well-document on reference making customize dashboard much harder
- More challenges on the way, but Google, StackOverFlow, Towards Data Science, and GitHub will always be your best friends
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