# Twitter Sentiment Analysis Project

## Overview:
This repository contains the source code for a machine learning project focused on sentiment analysis of Twitter data. Sentiment analysis is the process of determining the sentiment expressed in text data, such as tweets, which can be positive, negative. In this project, I aim to classify tweets into sentiment categories using supervised learning techniques.

## Features:
- Data Preprocessing: Text data preprocessing techniques, including lowercasing, removal of special characters and stopwords, tokenization, and TF-IDF vectorization.
- Model Training: Implementation of machine learning models, including Logistic Regression and Random Forest, for sentiment classification.
- Evaluation Metrics: Calculation and reporting of evaluation metrics such as accuracy, precision, recall, F1-score, and confusion matrix.
- Visualization: Visualization of evaluation metrics and performance using seaborn and matplotlib.

## Dataset:
The dataset used in this project is the "Twitter Sentiment Analysis" dataset from Kaggle, which can be found [here](https://www.kaggle.com/datasets/arkhoshghalb/twitter-sentiment-analysis-hatred-speech).

## Requirements:
- Python 3.x
- Required Python packages listed in the notebook

## Usage:
1. Download the Jupyter Notebook file (`Twitter_Sentiment_Analysis_Final.ipynb`) to your local machine.
2. Install the required Python packages listed in the notebook.
3. Obtain Twitter data or use the provided sample data.
4. Run the notebook cells for data preprocessing, model training, and evaluation.
5. Explore the results, evaluation metrics, and visualizations.
6. Modify the code or experiment with different models and parameters as needed.