MoodMeter

This is a Gradio web application for Sentiment Analysis using Random Forest model that has been trained to recognize emotions of text input. The model was trained using Scikit-learn, Spacy, and TfidfVectorizer.

Installation

Clone the repository and navigate to the root folder:

git clone https://github.com/yourusername/yourproject.git
cd yourproject

Create a virtual environment:

python3 -m venv venv

Activate the virtual environment:
On Windows:

venv\Scripts\activate

On Linux or macOS:

source venv/bin/activate

Install the dependencies:

pip install -r requirements.txt

Usage

To run the application, navigate to the root folder and execute the following command:

python app.py

Then, open a web browser and go to http://localhost:5000/. Write text in the text field and the application will predict the sentiment of your text using the pre-trained machine learning model.

Files

• app.py: This is the Gradio web application that serves as the main entry point of the program. It uses the machine learning model to predict the sentiment of the input field.

• main.py: This is the Python script that trains the machine learning model using Scikit-learn, Spacy, and TfidfVectorizer.

• model.pkl: This is the pre-trained machine learning model.

• dataset: This directory contain the data used for training the model.

• requirements.txt: This is the text files which contains all the necessary dependencies with their versions.

License

This project is protected by the MIT License. See the LICENSE file for more details.

Credits

• This project was created by Utsav Acharya.
• The face recognition model was trained using kaggle dataset jp797498e/twitter-entity-sentiment-analysis.
• The Spacy, and TfidfVectorizer were used for data preprocessing, cleaning and feature engineering of the text.
• The Scikit-learn was used for model training.
• The Gradio web framework was used to create the web application.