SentimentAnalysisML

This repository contains my research project focused on "Comparative Analysis of Classification Models on Text based Sentiment Analysis". We compare various classification models to analyze the sentiment expressed in text data and compared their performances.

Overview

Sentiment analysis, also known as opinion mining, is a Natural Language Processing (NLP) technique that involves determining the sentiment or opinion expressed in a piece of text. This project aims to explore different machine learning classification models for sentiment analysis and compare their performance.

Dataset

For this project, we used a Kaggle dataset of 1.6 million tweets.

Repository Structure

The repository is structured as follows:

TextClassificationSentimentAnalysis/
├── notebook/
│   ├── sentiment_analysis.ipynb
├── reports/
│   ├── research_paper.pdf
|   ├──  research_flow.jpg
│   ├── result.jpg
├── presentation/
|   ├── presentation.pdf 
├── README.md
├── LICENSE

• models/: Contains Python scripts implementing different classification models.
• utils/: Contains utility scripts for preprocessing, evaluation, and visualization.
• notebooks/: Contains Jupyter notebooks for exploratory analysis, model training, and model evaluation.
• reports/: Contains research papers and result summaries related to the project.
• README.md: The main readme file providing an overview of the project and instructions.
• requirements.txt: Specifies the dependencies required to run the project.
• LICENSE: The license file specifying the terms of use for the project.

Getting Started

1. Clone the Repository Start by cloning this repository to your local machine. You can do this by running the following command:

git clone https://github.com/PriyanshNegi/SentimentAnalysisText.git

Alternatively, you can download the repository as a ZIP file and extract it to your preferred directory.

2. Open the Notebook in Google Colab Go to the following link to open the notebook in Google Colab:

Sentiment Analysis Notebook

Click on "Open in Colab" to launch the notebook in Google Colab.

3. Set Up the Environment Once the notebook is open in Google Colab, you can run the cells to install any necessary dependencies or libraries. The notebook will guide you through the installation process if required.

4. Import and Analyze Data To perform sentiment analysis, you need text data. You can either import your own dataset or use the provided Kaggle dataset in the repository under Dataset. The notebook provides instructions on how to import and analyze the data, allowing you to understand its structure and gain insights.

5. Preprocess the Text Text preprocessing is a crucial step in sentiment analysis. The notebook includes code snippets to preprocess the text data, such as removing stopwords, tokenizing, and converting text into numerical representations suitable for machine learning models.

6. Train and Evaluate Models The notebook offers various machine learning models for sentiment analysis. You can choose a model that suits your requirements or experiment with multiple models. Train the selected model on your preprocessed data and evaluate its performance using appropriate metrics.

7. Interpret and Visualize Results Interpreting and visualizing the results of sentiment analysis is essential for understanding the sentiment expressed in the text data. The notebook provides code snippets for interpreting and visualizing the results, including generating word clouds, sentiment distributions, and accuracy scores.

8. Customize and Experiment Feel free to customize the notebook according to your specific requirements. You can experiment with different preprocessing techniques, feature engineering approaches, or machine learning algorithms to improve the sentiment analysis results.

9. Save and Share Once you have completed your analysis and achieved satisfactory results, save the modified notebook. You can share the notebook with others for collaboration or refer back to it in the future.

Conclusion

This repository provides a Google Colab notebook that enables you to perform sentiment analysis on text data using machine learning techniques. By following the steps outlined above, you can get started with sentiment analysis, analyze text data, train models, interpret sentiment, and visualize the results. Happy sentiment analysis!

Contributing

Contributions to enhance the functionality and performance of this sentiment analysis project are welcome. If you would like to contribute, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Develop your feature or bug fix.
4. Commit your changes and push them to your fork.
5. Submit a pull request explaining your changes.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Please feel free to reach out if you have any questions, suggestions, or concerns.