CodeAlpha Internship - Task 3: Sentiment Analysis

📌 Project Overview

This repository contains my submission for Task 3: Sentiment Analysis as part of the CodeAlpha Data Analytics Internship.
The goal of this task is to analyze text data (such as reviews or social media posts) and classify it as positive, negative, or neutral using Natural Language Processing (NLP) techniques.

🛠️ Tools & Libraries

• Python
• Pandas
• NLTK / TextBlob / VADER
• Scikit-learn
• Matplotlib / Seaborn

📂 Files in Repository

• Task4_Sentiment_Analysis.ipynb → Jupyter Notebook with code and explanations
• reviews_dataset.csv → Dataset used for sentiment analysis

🚀 Steps Performed

1. Loaded and preprocessed text data (tokenization, stopword removal, etc.).
2. Applied sentiment analysis using lexicon-based and machine learning approaches.
3. Classified text into positive, negative, or neutral categories.
4. Visualized sentiment distribution with bar charts and word clouds.
5. Interpreted results to understand public opinion and trends.

🎯 Key Insights

• Sentiment analysis helps identify customer opinions and emotions.
• Positive reviews highlight strengths, while negative reviews reveal areas for improvement.
• Neutral reviews provide balanced feedback and context.