TESSA | Text Sentiment Analysis

TESSA

TESSA is a Text Sentiment Analysis project with main purpose to predict the Emotion conveyed from a text document. This project explores machine learning models for text emotion detection and classification. We aim to develop an efficient model for identifying emotions in text data, employing natural language processing. The models trained on labeled Twitter(X) datasets find applications in customer service, social media monitoring, marketing analysis, and various other uses. We mainly hope to utilize this emotion detection as a marketing tool to analyze customer perceptions gathered from reviews, tweets, and customer service interactions. This approach will help clients understand their sentiments within their customer base, enabling targeted strategies aligned with their experiences and expectations. We examine and create eight models to determine the most effective approach for emotion detection. We successfully developed our business tool, with the Bidirectional LSTM emerging as the top-performing model with an accuracy of 92.88%.
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Table of Contents

1. About The Project
   • Application User Interface
   • Tasks
   • Dataset
   • Models Built
   • Built With
2. Getting Started
   • Prerequisites
   • Setup
3. Usage
4. Documents
5. Contributing
6. License
7. Contact

About The Project

Application User Interface

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Tasks

Task	Assigned
Create Github Repository	Everyone
Brainstorm Project	Everyone
Finding Dataset	Everyone
EDA	Everyone
Data Preprocessing	Everyone
Data Modeling: Random forest, Decision Tree	Meng Wai Chan
Data Modeling: Logistic Regression , SVM	Farhanul Thouship
Data Modeling: BiLSTM, CNN, CNN+LSTM, MNB	Georgios Ioannou
Model Evaluation	Everyone
Streamlit Application	Georgios Ioannou

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Dataset

Emotions Dataset For NLP - 2 Columns, 20k rows - Columns: content, sentiment

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Models Built (Click on the model that you are interested)

1. Bidirectional LSTM (BiLSTM)
2. Convolutional Neural Network (CNN)
3. CNN + LSTM
4. Multinomial Naive Bayes (MNB)
5. Decision Tree
6. Random Forest
7. Logistic Regression
8. Support Vector Machine (SVM)

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Built With

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Getting Started

To get a local copy of TESSA up and running locally follow these simple example steps:

Prerequisites

NOTE: How to check if Python is installed and what is its version

  python --version

NOTE: How to check if Git is installed and what is its version

  git -v

1. Please make sure you have pyenv installed and use Python3 version 3.11.0:

   • You can use pyenv to switch between different Python versions:
     • Windows: https://www.youtube.com/watch?v=HTx18uyyHw8
     • Mac: https://www.youtube.com/watch?v=31WU0Dhw4sk
     • Linux: https://www.youtube.com/watch?v=1Zgo8M9yUtM

2. Please make sure you have git installed

   • Windows: https://git-scm.com/download/win
   • Mac: https://git-scm.com/download/mac
   • Linux: https://git-scm.com/download/linux

3. Please look at the setup folder found in this project.

Setup

1. Navigate to the directory where you want to clone/run/save the application:

   cd your_selected_directory

2. Clone this repository:

   git clone https://github.com/mengwaichan/CSc448_Final_Project.git

3. Navigate to the realesrgan git repository:

   cd CSc448_Final_Project

4. Use Python3 3.11.0 version in the cloned repository folder:

   pyenv local 3.11.0

5. Create virtual environment in the cloned repository folder:

   python -m venv .tessa-venv

6. Activate the virtual environment (Windows OR Mac/Linux):

   1. Windows

   .\.tessa-venv\Scripts\activate

   2. Mac/Linux

   source .tessa-venv/bin/activate

7. Install the dependencies listed in the requirements.txt file:

   pip install -r requirements.txt

8. Run Streamlit:

   streamlit run app.py

9. To Run The Notebooks (3 Options):

   1. Use Kaggle
   2. Use Jupyter Notebboks Extension for VS Code
   3. Use Anaconda
      1. Download and install Anaconda
      2. Launch a jupyter notebook:
         1. MacOS users, open up terminal and type in jupyter notebook
         2. Window users, open up your Anaconda Power Shell, and type in jupyter notebook

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Documents

Machine Learing Model is located in code

Project Presentation is located in documents

Project Final Report is located in documents

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

MIT License

Copyright (c) 2023 TESSA

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Contact

Georgios Ioannou - @LinkedIn

Farhanul Thouship - @LinkedIn

Meng Wai Chan - @LinkedIn

Project Link: https://github.com/mengwaichan/CSc448_Final_Project

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