Sentiment analysis with LSTM architecture - Pytorch

Project Structure

• Dataset: This directory contains the dataset files used for training and evaluation.
• model.py: This file contains the relevant piece of code required to run the model for inference after training.
• train.py: You train the modle by running this script. If you make any hyperparam changes in the model.py file make sure to make those changes here as well.
• requirements.txt: requirements file to automate the process of installing the required dependencies.
• model_test.py: This is the script you'll run to test the model on your own text data.

Dependencies

• Python 3.9 or higher
• numpy
• pandas
• scikit-learn
• tensorflow
• keras
• torch
• torchtext
• tweet-preprocessor
• pickle

Installation

• Open the terminal in your code editor and type this in
  git clone https://github.com/GraphicsMonster/LSTM-sentiment-analysis-model
• To install the required dependencies, type this in
  pip install -r requirements.txt
• Once the dependencies are installed you are ready to train the model and evaluate its performance. If you have your own data to train the model on, you can update the code in the model.py to refer to the location of your dataset on your local machine. Be sure to update the preprocessing steps accordingly!!
• Train the model run this command in the terminal
  python train.py
• Once you've successfully trained the model, it will automatically be saved in the same directory with the name model.pt
• Test the model on your own text data
  python model_test.py

Contributing

License