🎭 Decoding Emotions Through Sentiment Analysis of Social Media Conversations
📌 Project Title
Decoding Emotions Through Sentiment Analysis of Social Media Conversations
🎯 Objective
To build an AI-powered emotion detection system that can classify emotions such as joy, sadness, anger, fear, etc., from social media text using Natural Language Processing (NLP) techniques and machine learning algorithms like SVM and Logistic Regression.
🧠 Overview
This project aims to automate the identification of human emotions expressed in social media posts. With the vast and growing amount of unstructured data online, manual analysis is impractical. The system uses NLP to clean and transform text data, applies sentiment classification using various models, and outputs the detected emotion. The solution is scalable and useful for businesses, researchers, and mental health studies.
🔍 Key Features
- Multi-class emotion classification (joy, sadness, anger, fear, etc.)
- AI-driven sentiment analysis using NLP and machine learning
- Clean and reusable pipeline for data preprocessing and model training
- Deployable using Streamlit or Gradio for real-time emotion prediction
- Easy experimentation in Google Colab or Jupyter Notebook
📊 Dataset
Dataset Used: Kaggle - Emotions Dataset for NLP
Contains:
- Social media text data
- Emotion labels (joy, anger, sadness, fear, surprise, love)
- Balanced and pre-cleaned dataset ideal for NLP classification tasks
⚙️ Technologies Used
- Python
- Pandas, NumPy
- Scikit-learn, TensorFlow (optional), Transformers (Hugging Face)
- NLTK / re (for text preprocessing)
- Matplotlib, Seaborn (for visualizations)
- Streamlit / Gradio (for deployment)
🤖 Algorithms Implemented
- Logistic Regression – baseline model for classification
- Support Vector Machine (SVM) – for high-performance text classification
- Random Forest – for ensemble-based emotion classification
- BERT – state-of-the-art transformer model for semantic understanding
- TF-IDF Vectorizer – for transforming text into numeric features
- Pretrained Tokenizers – for deep learning models like BERT
🚀 Deployment
The model is tested and implemented on Google Colab and is deployable using:
- Streamlit Cloud
- Gradio + Hugging Face Spaces
- Flask on Render or Deta
👥 Team Members
This project was created by the team under the Naan Mudhalvan initiative:
- Rishi Easwaran A R
- Sabdhar Ali
- Saferuden B
- Riyaz Ahamed
📌 Future Enhancements
- Integrate multilingual emotion classification
- Add real-time data analysis from Twitter API
- Build a feedback loop to improve model accuracy with user inputs
- Use NLP for sarcasm and sentiment detection in complex sentences
📎 References