Social Media Sentiment Analysis Dashboard
A natural language processing pipeline and dashboard that monitors social media discourse, reviews, and tweets to classify sentiment into positive, negative, or neutral categories. The system incorporates text tokenization, stop-word removal, and TF-IDF vectorization paired with Logistic Regression classifiers, helping brand managers track customer perception and detect negative sentiment spikes in real time.
Technical Stack & Runtime
Verified languages, toolkits, and algorithmic modules implemented in this system:
Engineering Blueprint
- Production-tested code with modular dependency isolation
- Lightweight deployment without excessive GPU/hardware overhead
- Direct public repository verification on Shrayu Technologies GitHub
- Compatible with enterprise microservice and event-driven architectures
AI & Machine Learning Solutions
Cognitive Pipelines, LLMs & Computer Vision. Discover how our engineering team provisions architectures like this in enterprise environments.
Related Production Case Studies
Other verified repositories in Machine Learning:
Credit Card Fraud Detection System
An enterprise machine learning pipeline specialized in identifying fraudulent credit card transactions in heavily imbalanced financial datasets. The project uses SMOTE (Synthetic Minority Over-sampling Technique) to handle class imbalances and trains Random Forest and Logistic Regression models evaluated on Precision-Recall curves and ROC-AUC. It includes a live transaction simulation engine that flags suspicious purchases with sub-second inference.
Customer Churn Prediction Model
An end-to-end customer retention modeling pipeline that predicts subscription and account cancellations before they occur. By analyzing customer tenure, contract types, service usage volume, and payment histories, the XGBoost classification model calculates individual churn probabilities. The system empowers customer success teams to deploy targeted retention campaigns and identify vulnerable revenue streams early.
House Price Prediction using Regression
A residential property valuation system that models housing prices across Indian urban real estate markets using regression algorithms. The project implements data cleaning, outlier removal, and feature engineering for square footage, locality scores, and amenities. It compares Linear Regression, Decision Trees, Random Forests, and XGBoost models to provide accurate, data-backed real estate valuation estimates.
Compare This System Against Alternative Architectures
Use our technical comparison engine to benchmark Social Media Sentiment Analysis Dashboard against other production systems in algorithmic complexity, throughput, and hardware requirements.
