Brand Engagement Tracker: Twitter/X Monitoring and Insights

This project aimed to create a robust solution for monitoring and visualizing brand engagement on Twitter/X, allowing clients to compare their performance against competitors and identify key areas for improvement. Below is a detailed breakdown of the solution:

Ingestion: Implemented a real-time data pipeline using the Twitter/X API to continuously fetch tweets related to the client’s brand and competitors.

Data Streaming: Leveraged NiFi for orchestrating the flow of data and Kafka to establish a reliable streaming pipeline.

Programming: Wrote custom scripts in Python for tweet preprocessing, ensuring data was clean and structured for further analysis.

Developed and trained machine learning models to perform sentiment analysis on the collected tweets, categorizing them into positive, negative, or neutral sentiments.

Stored the sentiment results alongside the original tweets in a centralized database for easy retrieval and analysis.

Created a RESTful API using Node.js + Express.js, which served as the backend for the dashboard and handled data retrieval efficiently.

Designed the front-end interface using Angular, delivering a smooth and responsive experience for end users.

The dashboard provided:

The final product enabled clients to:

Gain real-time insights into how their brand was perceived on Twitter/X.

Compare their performance with competitors to understand market positioning.

Identify strengths and areas of improvement for strategic planning.

The solution combined state-of-the-art technologies and an intuitive user interface to bridge the gap between complex data pipelines and actionable business insights.