https://github.com/shubhamahobia/rnn-classification

This project involves building a sentiment analysis model using Recurrent Neural Networks (RNN) to classify movie reviews from the IMDb dataset as either positive or negative. The IMDb dataset consists of 50,000 highly polarized movie reviews, with 25,000 labeled as positive and 25,000 as negatives.
https://github.com/shubhamahobia/rnn-classification

keras-tensorflow machine-learning nlp python recurrent-neural-network recurrent-neural-networks simplernn tensorflow

Last synced: 27 days ago
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This project involves building a sentiment analysis model using Recurrent Neural Networks (RNN) to classify movie reviews from the IMDb dataset as either positive or negative. The IMDb dataset consists of 50,000 highly polarized movie reviews, with 25,000 labeled as positive and 25,000 as negatives.

• Host: GitHub
• URL: https://github.com/shubhamahobia/rnn-classification
• Owner: ShubhaMahobia
• Created: 2024-08-23T16:58:36.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-08-25T21:31:05.000Z (2 months ago)
• Last Synced: 2024-10-11T04:41:34.025Z (27 days ago)
• Topics: keras-tensorflow, machine-learning, nlp, python, recurrent-neural-network, recurrent-neural-networks, simplernn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://rnn-classification-4qyd35scpyeq4u9xjycxmd.streamlit.app/
• Size: 13.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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![Logo](https://miro.medium.com/v2/resize:fit:828/format:webp/1*PKXTGLCkzRqfzJaYJV4X9g.png)

# RNN - IMDB Dataset Review Classification

This project involves building a sentiment analysis model using Recurrent Neural Networks (RNN) to classify movie reviews from the IMDb dataset as either positive or negative. The IMDb dataset consists of 50,000 highly polarized movie reviews, with 25,000 labeled as positive and 25,000 as negative, making it an ideal dataset for binary sentiment classification tasks.

## Live Deployment 

This project is hosted on - https://rnn-classification-4qyd35scpyeq4u9xjycxmd.streamlit.app/

## Run Locally

1. Clone this repo into your system.

2. Create virtual environment using the command -

```bash

 conda create -p myenv python==3.9.0

```

3. Now install all the packages which are listed in requirements.txt

```bash

 pip install -r requirements.txt

```

4. Now run all the cell in the Experiments.ipynb And Prediction.ipynb as per your need.

5. To run on streamlit - 

```bash

    streamlit run main.py

```

## Tech Stack

**Frontend Client:** Streamlit Services

**Model Used:** RNN - Recurrent Neural Network

**Dataset Used:** IMDB Dataset  

## Feedback

If you have any feedback or just to say Hi!, please reach out to me at [email protected]

## Authors

- [@ShubhaMahobia](https://github.com/ShubhaMahobia)