Link to download CNN model https://drive.google.com/drive/folders/16Eud9aZPFP3B0hItvAI6cUTElFi8yLce?usp=drive_link Download the model CNN model and place it into root folder
Dataset Dataset link :https://www.kaggle.com/datasets/ferno2/training1600000processednoemoticoncsv
You can build the CNN model on your custom dataset using the provided notebook.
Steps to run the code Here are the steps you can provide for users to run the code from your GitHub repository:
1. Clone the Repository:
Use Git to clone the repository to your local machine.
git clone <repository_url.git>
2. Navigate to the Project Directory: Use the cd command to move into the project directory.
cd <project_directory>
3. Set Up the Environment: #Create a virtual environment to manage dependencies (optional but recommended).
python -m venv env
4. Activate the virtual environment.
Windows: .\env\Scripts\activate
Mac/Linux: source env/bin/activate
5. Install Required Packages: Use pip to install the necessary packages listed in the requirements.txt file.
pip install -r requirements.txt Download Model and Tokenizer: Users need to have the pre-trained model, tokenizer, and other necessary files. Ensure they are included in your repository or specify instructions on where to download them from.
6. Run the Streamlit App: Execute the Streamlit app script.
streamlit run app.py Replace app.py with the name of your main Python script containing the Streamlit code if it's named differently.
Access the App: Once the Streamlit app is running, it should provide a local URL (usually http://localhost:8501). Open this URL in a web browser to access the Twitter sentiment analysis tool.