Uncovering twitter trends through sentiment analysis
We developed a real-time sentiment analysis system for Twitter (X) trends using Kafka and Spark. This project's aim is to leverage data-driven business insights to help companies assess and forecast public sentiment on trending topics.
This project aims to bridge the gap between data and people, offering a unique window into data-driven analytics and enhancing businesses' ability to make informed decisions based on real-time public opinion.
Trendz-Insights is a project that aims to analyze trends on social media, particularly Twitter, using a pipeline of technologies including Kafka, Spark Streaming, PostgreSQL, Power BI, and Spring Boot. The project utilizes external NLTK for NLP based sentiment analysis of tweets and trends, and TF-IDF for trend generation.
- Emulation of twitter API
- Apache Kafka for high throughput asynchronous message queue.
- Apache Spark as data processing engine.
- PostgreSQL: for data storage.
- NLTK for sentiment generation.
- Spark MLlib for trend generation usng TF-IDF algoriCthm.
- Nishant Tanksale https://www.linkedin.com/in/nishant-tanksale/
- Sarvadnyaa Barate https://www.linkedin.com/in/sarvadnyaa-barate/