In today’s digital world, social media platforms are a goldmine of opinions, emotions, and customer feedback. Understanding public sentiment is crucial for businesses, brands, and organizations to make data-driven decisions.
This project focuses on Sentiment Analysis of Social Media Data using Natural Language Processing (NLP)techniques. The model classifies text into Positive, Negative, or Neutral sentiments, helping uncover audience insights in real time.
🛠 Tech Stack
Python – core programming language
Transformers (Hugging Face) – pre-trained sentiment analysis model
Streamlit – interactive web app for visualization
Pandas & NumPy – data handling and preprocessing
Matplotlib/Plotly – data visualization
⚙️ How It Works
Data Input – Users enter or upload social media text data.
Preprocessing – The pipeline tokenizes and cleans text.
Model Prediction – A transformer model predicts sentiment.
Visualization – Results are displayed in charts (positive, negative, neutral distribution).
📊 Features
🔹 Real-time sentiment prediction
🔹 Interactive Streamlit dashboard
🔹 Handles large text datasets efficiently
🔹 User-friendly interface with data upload support
🔹 Clean and scalable code for future improvements
💡 Use Cases
Recruiters/HR – Analyze employee feedback or Glassdoor reviews
Brands – Track customer sentiment on Twitter/Instagram
Politics – Gauge public opinion during campaigns
Market Research – Discover consumer attitudes towards products
🎯 Outcome
This project demonstrates practical Data Science and NLP skills, combining machine learning, data engineering, and web development. It highlights my ability to build end-to-end solutions that can scale from prototype to production.
📌 Why Recruiters Should Care
Recruiters looking for Data Scientists, Machine Learning Engineers, or NLP specialists will find this project relevant because it shows:
Hands-on expertise in NLP & Transformers
Experience with interactive dashboards & deployment
Ability to translate raw text data into actionable business insights
🔗 Project Links
