Sentiment Analyzer on Twitter Dataset

This project is a Sentiment Analysis system built using Natural Language Processing (NLP) techniques and a Logistic Regression classifier. It analyzes tweets to determine whether their sentiment is positive or negative, based on a dataset of over 1.5 million tweets.

🔍 Overview

• Model Type: Logistic Regression
• Vectorization: TF-IDF
• Dataset: 1,599,999 tweets × 6 columns
• Accuracy: 79%
• Deployment: Streamlit Web App

📊 Dataset

The dataset used contains 1.6 million tweets, each labeled with its sentiment:

• Positive (1) or Negative (0)
• Columns include: sentiment, tweet ID, date, flag, user, text

🛠️ Tools & Libraries

• Python
• Pandas, NumPy
• scikit-learn
• NLTK
• Streamlit

⚙️ Features

• Clean and preprocess tweet text (remove stopwords, tokenize, etc.)
• Convert text to numeric form using TfidfVectorizer
• Train and evaluate a Logistic Regression model
• Show accuracy, confusion matrix, and visualizations
• Streamlit interface to predict sentiment of custom input tweets

🚀 Getting Started

1. Clone the Repository

git clone https://github.com/akhilkumar-dot/sentiment-analyzer.git
cd sentiment-analyzer