Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture used in deep learning. LSTMs are specifically designed to handle long-term dependencies in data, making them well-suited for tasks involving text data, speech, and time series. In this project, we build an LSTM model to classify app reviews on a scale of 1 to 5 based on user feedback using PyTorch.
To build text classifier to classify app reviews on a scale of 1 to 5 using LSTM.
The dataset consists of app reviews and corresponding ratings. The "score" column contains ratings in the range of 1 to 5, and the "content" column contains the review text.
- Language:
Python
- Libraries:
pandas
,TensorFlow
,matplotlib
,scikit-learn
,NLTK
,NumPy
,PyTorch
- Lowercasing text, removing punctuation, and eliminating links.
- Balancing classes.
- Tokenizing the text.
- Scaling the data.
- Training an LSTM model in PyTorch.
- Evaluating the model on test data.
-
Input: Contains the data used for analysis, including:
- [List of data files]
-
ML_Pipeline: This folder contains functions distributed across multiple Python files, each appropriately named for its functionality. These functions are called from the
Engine.py
file. -
Notebook: Contains the Jupyter Notebook file of the project.
-
Engine.py: The main script that orchestrates the different parts of the project by calling functions from the ML Pipeline.
-
Readme.md: Instructions for running the code and additional information about the project.
-
requirements.txt: Lists all the required libraries and their versions for easy installation using
pip
.