Real-Time Social Media Sentiment Analytics Dashboard using Databricks Lakehouse Architecture

Debasmita Biswal
Department of Master of Computer Applications

GIFT Autonomous, Bhubaneswar, Odisha, India, debasmitab2024@gift.edu.in

Smruti Ranjan Swain
Head of Department Master of Computer Applications

GIFT Autonomous, Bhubaneswar, Odisha, India, hodmca@gift.edu.in

Abstract—Social media platforms generate a massive volume of user-generated content every day in the form of posts, comments, likes, and reviews. Analyzing this large amount of social media data is important for understanding public opinion, customer behavior, market trends, and sentiment patterns. This paper presents a Social Media Sentiment Analytics Dashboard developed using the Databricks Lakehouse Platform and Medallion Architecture for scalable social media data processing and analysis. The proposed system processes social media datasets through Bronze, Silver, and Gold layers to improve data quality, perform transformation, and generate analytical insights. Interactive dashboards are used to visualize sentiment distribution, engagement trends, user activity, hashtag analysis, and overall social media performance using various charts and analytical reports. The developed system helps organizations, analysts, and decision-makers identify sentiment patterns, monitor audience engagement, and support data-driven business decisions. Furthermore, this work demonstrates how modern big data technologies and visualization tools can be effectively utilized for intelligent social media analytics and sentiment management.

Keywords— Social Media Analytics; Sentiment Analysis; Databricks; Medallion Architecture; Big Data Analytics; Dashboard Visualization; Lakehouse Platform; Data Visualization; Social Media Monitoring; Business Intelligence