Real-time Twitter Sentiment Analysis for Brand Improvement and Topic Tracking (C...

Deploy a Real-time Twitter Analytical Web App on Heroku using Dash & Plotly in Python

This tutorial will teach you 1) how to deploy all data analytics with insights on the Heroku cloud application platform and 2) how to migrate Plotly -based data visualization to analytical dashboard web app using Dash in Python.

Note: Real-time Twitter Data Collection and Data Analytics & Sentiment Analysis were completed in previous chapters.

• 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 (You’re here !): Deploy a Real-time Twitter Analytical Web App on Heroku using Dash & Plotly in Python
• Chapter 4 (Optional) : Parallelize Streaming Twitter Sentiment Analysis using Scala, Kafka and Spark Streaming

Why Dash?

• Dash is a productive Python framework for building web applications. Written on top of Flask , Plotly.js , and React.js , Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.
• Dash Core Components (dcc) provide supercharged components for interactive user interfaces.
• Dash Html Components (html) provide pure Python abstraction around HTML, CSS, and JavaScript.

Why Heroku?

• Heroku is a platform as a service (PaaS) that enables developers to build, run, and operate applications entirely in the cloud.

In order to run the real-time twitter monitoring system, we will use two scripts (or two dynos, or two apps). One is used for collecting the streaming data , and another one is used for data analysis and visualization in real-time. This approach could effectively reduce the latency of data pipeline when handling high-throughput Twitter textual data.