Airline-Tweet-Sentiment-Analysis-using-NLP

This project implements sentiment analysis on airline tweets using Natural Language Processing (NLP) and Random Forest Classifier.

Project Overview

The model analyzes tweets to classify them into three sentiment categories:

• Positive
• Neutral
• Negative

Technologies Used

• Python 3.13
• NLTK
• scikit-learn
• pandas
• numpy
• matplotlib
• Regular Expressions (re)

Model Performance

• Accuracy: 75.92%
• Algorithm: Random Forest Classifier
• Features: 2500 TF-IDF features

Installation

• pip install nltk
• pip install scikit-learn
• pip install pandas
• pip install numpy

Data Processing Steps

1. Text preprocessing:

   • Special character removal
   • Single character removal
   • Multiple space removal
   • Lowercase conversion

2. Feature extraction:

   • TF-IDF Vectorization
   • Stop words removal
   • Feature selection (max_features=2500)

3. Model Training:

   • 80-20 train-test split
   • Random Forest with 200 estimators

Usage

1. Load your dataset in CSV format
2. Run the preprocessing pipeline
3. Train the model
4. Make predictions on new data

Model Parameters

• TF-IDF Parameters:

  • max_features: 2500
  • min_df: 7
  • max_df: 0.8

• Random Forest Parameters:

  • n_estimators: 200
  • random_state: 0