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Cloud Analytics: UCL Insights via BigQuery, NLP & Dashboard
Project type
Cloud Analytics, Dashboard
🥅 This is a project that leverages online analytical processing (OLAP) through a cloud data warehouse to analyze YouTube comments from beIN SPORTS Asia for UEFA Champions League (UCL) matches in the 3rd week of March 2025. Utilizing advanced data analytics, the project classifies fan sentiments and identifies key discussion topics, aiming to uncover regional interests and match engagement levels with a focus on data-driven insights.
🥅 This project empowers sports broadcasters and marketers with a deeper understanding of fan sentiments and preferences, shifting from subjective guesses to objective analysis. It holds the potential to transform content strategy and advertising by delivering precise, data-backed insights, enabling targeted engagement and revenue optimization.
🥅 The process begins with scraping over YouTube comments using Python library, Selenium, targeting videos of UCL matches played. The raw data undergoes preprocessing to remove noise and gets stored in Google Cloud BigQuery, a serverless data warehouse.
🥅 The analysis proceeds with Cloud Natural Language API to perform sentiment analysis, categorizing comments into positive, neutral, and negative sentiments while assessing their intensity. Feature extraction via TF-IDF generates word clouds, and Latent Dirichlet Allocation (LDA) applies unsupervised topic modeling to reveal dominant themes, such as match outcomes and player performances, across regions.
🥅 The insights culminate in an interactive dashboard created with Looker Studio, visualizing public sentiment, trends, comment distributions, and match popularity. This step presents the data intuitively, enabling stakeholders to explore fan reactions and make informed decisions based on the analytics.
Tech Stack:
- Data collection and preprocessing: Python, Selenium
- Google Cloud Toolkit: Google Cloud BigQuery, Google Cloud Natural Language API, Looker Studio
- Data analytics and visualization: Serverless data warehousing (BigQuery for OLAP), Sentiment analysis (NLP), Unsupervised Machine Learning (LDA topic modeling), Data Visualization (Looker Studio)
