An end-to-end Natural Language Processing (NLP) project that classifies text into positive, negative, or neutral sentiments. This project demonstrates the complete machine learning lifecycle: from data preprocessing and text vectorization to model training, evaluation, and API deployment.
- Robust Text Preprocessing: Handles stop words, lemmatization, and tokenization to clean noisy text data.
- Multiple Model Architectures: Compares baseline models (like Logistic Regression/Naive Bayes) against advanced architectures.
- Interactive API: Includes a lightweight web interface/API built with [Flask/FastAPI/Streamlit] to serve real-time sentiment predictions.
- Scalable Pipeline: Modular code structure designed for easy integration into larger applications.
- Language: Python
- Data Processing: Pandas, NumPy, Regex
- NLP & ML: NLTK, Scikit-Learn, TensorFlow/PyTorch
- Deployment: FastAPI / Flask / Streamlit
- Data Visualization: Matplotlib, Seaborn
The model was trained on the [Name of Dataset, e.g., IMDB Movie Reviews / Twitter Sentiment] dataset, which contains [Number] of text samples labeled with their corresponding sentiment.
- Clone the repository:
git clone [https://github.com/up65akhil/Sentiment-Analysis.git](https://github.com/up65akhil/Sentiment-Analysis.git) cd Sentiment-Analysis