Sentiment Analysis Project

Overview

This repository contains a comprehensive Sentiment Analysis project designed to classify text data into three sentiment categories: positive, negative, and neutral. Using a variety of machine learning models and natural language processing (NLP) techniques, this project leverages popular libraries such as NumPy, Pandas, Matplotlib, Seaborn, and NLTK for data manipulation, visualization, and analysis.

Features

• Data Preprocessing: Cleans and prepares text data, including tokenization, stopword removal, and lemmatization.
• Model Training: Implements multiple machine learning algorithms, allowing comparison across models. Models included:
  • Logistic Regression
  • Naïve Bayes Classifier
  • Support Vector Classifier (SVC)
  • Decision Trees
  • Random Forest Classifier
• Model Evaluation: Assesses performance using key metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
• Visualization: Generates visualizations, including word clouds for each sentiment category, to provide insights into the most common words associated with each sentiment.

Installation

To get started with this project, follow these steps:

1. Clone the Repository:

   git clone https://github.com/yourusername/sentiment-analysis.git
   cd sentiment-analysis

2. Install Dependencies: Ensure you have Python 3.x installed. Use the following command to install required libraries:

   pip install -r requirements.txt

Requirements

• Python 3.x
• Jupyter Notebook
• Required Libraries:
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • nltk
  • scikit-learn
  • wordcloud

Usage

1. Data Loading: Load your dataset into the Jupyter Notebook. Ensure the dataset has a column for text data and one for sentiment labels.
2. Preprocessing: Run preprocessing steps to clean, tokenize, and lemmatize the text data.
3. Model Training: Choose and train a model from the available options. Adjust hyperparameters as needed.
4. Evaluation: Evaluate model performance using metrics such as accuracy, F1-score, and AUC-ROC.
5. Visualization: Generate word clouds and other visualizations to analyze sentiment distribution within the data.

Example Code

The following example demonstrates how to load a dataset and generate a word cloud for negative sentiment words:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud, STOPWORDS

# Load your dataset
df = pd.read_csv('data.csv')

# Generate a word cloud for negative sentiment
negative_df = df[df['sentiment'] == 'negative']

# Function to generate a word cloud
def generate_wordcloud(data, title):
    text = " ".join(review for review in data['text'])
    wordcloud = WordCloud(stopwords=STOPWORDS, background_color="white").generate(text)
    
    plt.figure(figsize=(10, 6))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.title(title)
    plt.axis("off")
    plt.show()

generate_wordcloud(negative_df, 'Negative Sentiment WordCloud')

Contributing

Contributions are welcome! If you'd like to contribute, please follow these steps:

1. Fork the Repository.
2. Create a New Branch:

   git checkout -b feature-branch

3. Make Your Changes and commit them:

   git commit -m 'Add new feature'

4. Push to the Branch:

   git push origin feature-branch

5. Create a New Pull Request.

Acknowledgments

• The Jupyter Development Team for JupyterLab.
• Contributors of the libraries used in this project, such as NLTK, scikit-learn, and Seaborn.
• [Insert any other acknowledgments here].

Contact

For questions or suggestions, feel free to reach out:

• Your Name - [email protected]
• GitHub: G.M.Ravindu Dulshan