https://github.com/alihassanml/imdb-sentimenal-analysis-simple-rnn
This repository contains a Streamlit application for predicting the sentiment of movie reviews using a pre-trained Simple RNN model. The model has been trained on the IMDB dataset, and the application allows users to input their own reviews to get sentiment predictions.
https://github.com/alihassanml/imdb-sentimenal-analysis-simple-rnn
Last synced: 2 months ago
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This repository contains a Streamlit application for predicting the sentiment of movie reviews using a pre-trained Simple RNN model. The model has been trained on the IMDB dataset, and the application allows users to input their own reviews to get sentiment predictions.
- Host: GitHub
- URL: https://github.com/alihassanml/imdb-sentimenal-analysis-simple-rnn
- Owner: alihassanml
- License: mit
- Created: 2024-08-03T17:43:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-03T18:22:40.000Z (about 1 year ago)
- Last Synced: 2025-02-21T11:27:01.101Z (8 months ago)
- Language: Jupyter Notebook
- Size: 14 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IMDB Sentimental Analysis Simple RNN
This repository contains a Streamlit application for predicting the sentiment of movie reviews using a pre-trained Simple RNN model. The model has been trained on the IMDB dataset, and the application allows users to input their own reviews to get sentiment predictions.
## Author
- **Ali Hassan**
## Features
- **Sentiment Analysis**: Predicts whether a given movie review is positive or negative.
- **Review Decoding**: Converts the encoded review back to its original text form for better visualization.
## Prerequisites
Make sure you have the following packages installed:
- `streamlit`
- `tensorflow`
- `numpy`
You can install them using:
```sh
pip install streamlit tensorflow numpy
```
## How to Run the Application
1. **Clone the repository**:
```sh
git clone https://github.com/alihassanml/IMDB-Sentimental-Analysis-Simple-RNN.git
cd IMDB-Sentimental-Analysis-Simple-RNN
```
2. **Download the pre-trained model**:
Make sure to place your `RNN_model.h5` file in the root directory of the cloned repository.
3. **Run the Streamlit application**:
```sh
streamlit run app.py
```
4. **Usage**:
- Enter a movie review in the provided text area.
- Click on the "Predict Sentiment" button to get the sentiment prediction (Positive or Negative).
- Optionally, click on the "Decode Review" button to see the encoded review converted back to text.
## Code Overview
### app.py
This is the main application file containing the following key functions and components:
- **Loading the pre-trained model**:
```python
my_model = load_model('RNN_model.h5')
```
- **Preprocessing and decoding functions**:
```python
def decode_review(encoded_review):
return ' '.join(Reverse_word.get(i - 3, '?') for i in encoded_review)
def preprocess_text(text):
words = text.lower().split()
encode_review = [word_index.get(word, 2) + 3 for word in words]
return sequence.pad_sequences([encode_review], maxlen=max_len)
```
- **Predict sentiment function**:
```python
def predict_sentiment(text):
encoded_review = preprocess_text(text)
prediction = my_model.predict(encoded_review)[0][0]
if prediction > 0.5:
return 'Positive'
else:
return 'Negative'
```
- **Streamlit app components**:
```python
st.title("Movie Review Sentiment Analysis")
st.write("Enter a movie review to predict its sentiment:")
review_text = st.text_area("Movie Review", "This movie was fantastic! The acting was great and the plot was thrilling")
if st.button("Predict Sentiment"):
sentiment = predict_sentiment(review_text)
st.write(f"Sentiment: {sentiment}")
if st.button("Decode Review"):
encoded_review = preprocess_text(review_text)[0]
decoded_review = decode_review(encoded_review)
st.write(f"Decoded Review: {decoded_review}")
```
## License
This project is licensed under the MIT License.