NLP for Sentiment Analysis

This project, completed as part of my internship at YBI Foundation, focuses on building a sentiment analysis model using Natural Language Processing (NLP) techniques to classify text into positive, negative, or neutral sentiments.

📌 Project Overview

The aim of this project is to process raw textual data, extract meaningful features, and train machine learning models to accurately predict sentiment. It demonstrates the practical use of NLP in real-world applications like product reviews, social media analysis, and customer feedback systems.

🔹 Key Steps

• Data Preprocessing – Cleaning text, removing stopwords, tokenization, stemming/lemmatization.
• Feature Extraction – Converting text into numerical vectors using Bag of Words and TF-IDF.
• Model Training – Implemented classification algorithms such as Logistic Regression and Naive Bayes.
• Evaluation – Measured accuracy, precision, recall, and F1-score for performance comparison.
• Visualization – Data distribution, word clouds, and confusion matrices for insights.

🛠 Tech Stack

• Languages: Python
• Libraries: Pandas, NumPy, NLTK, Scikit-learn, Matplotlib, Seaborn

📂 Use Cases

• Social media sentiment monitoring
• Product review analysis
• Customer feedback classification

🚀 How to Run

1. Clone this repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name