Sentiment Analysis of Social Media Posts

📖 Project Overview

This project performs sentiment analysis on social media posts (like tweets or Facebook posts) using Natural Language Processing (NLP) and Machine Learning (ML) techniques. It classifies text into positive, negative, or neutral sentiments to provide valuable insights into customer opinions, brand reputation, and trending topics.

🛠️ Features

Real-time and batch data collection from social media APIs

Text preprocessing (cleaning, tokenization)

Feature extraction using TF-IDF and Word Embeddings

Model training using SVM, Naive Bayes, and Deep Learning (LSTM, BERT)

Sentiment prediction and visualization

Modular, scalable, and easy to integrate

⚙️ Tech Stack

Programming Language: Python

Libraries:

NLP: NLTK, spaCy

ML: Scikit-learn, TensorFlow/Keras

Data Handling: Pandas, NumPy

Visualization: Matplotlib, Seaborn

APIs: Tweepy (for Twitter API)

Deployment (optional): Streamlit / Flask for Web App

✨ Future Enhancements

Add multilingual sentiment analysis

Improve model accuracy using fine-tuned BERT models

Build an interactive dashboard with real-time updates

Deploy the model using AWS/GCP cloud services