You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

LSTM IMDB Sentiment Analysis - Baseline Model

Traditional LSTM model for binary sentiment classification achieving 89% accuracy on IMDB movie reviews.

This model serves as a baseline in a comparative study demonstrating the superiority of transfer learning (DeBERTa: 96%) over traditional approaches.

Quick Start

import pickle
from tensorflow import keras
from huggingface_hub import hf_hub_download

# Download
model_path = hf_hub_download("radwa-f/LSTM-IMDB-SentimentAnalysis", "model.h5")
tok_path = hf_hub_download("radwa-f/LSTM-IMDB-SentimentAnalysis", "tokenizer.pkl")

# Load
model = keras.models.load_model(model_path)
with open(tok_path, 'rb') as f:
    tokenizer = pickle.load(f)

# Predict
text = "This movie was amazing!"
seq = tokenizer.texts_to_sequences([text])
padded = keras.preprocessing.sequence.pad_sequences(seq, maxlen=200)
pred = model.predict(padded)[0][0]

print(f"{'POSITIVE' if pred > 0.5 else 'NEGATIVE'} ({pred:.2%})")

Performance Comparison

Model	Accuracy	Type
LSTM (this)	89%	Traditional RNN
DeBERTa	96%	Transfer Learning
Difference	-7%

Architecture

Embedding(vocab_size, 128)
LSTM(128)
Dense(64, relu)
Dropout(0.5)
Dense(1, sigmoid)

Training

Dataset: IMDB (50K reviews)
Framework: Keras/TensorFlow
Loss: Binary Crossentropy
Optimizer: Adam

Model Creators

Radwa Fattouhi

Final-year Software Engineering Student
École Nationale des Sciences Appliquées (ENSA), El Jadida, Morocco
Email: fattouhiradwa@gmail.com
LinkedIn: radwa-fattouhi
GitHub: radwa-f

Amine Boktaya

Final-year Software Engineering Student
École Nationale des Sciences Appliquées (ENSA), El Jadida, Morocco
Email: amineboktaya1@gmail.com
LinkedIn: amine-boktaya
GitHub: BoktayaAmine

Citation

If you use this model in your research, please cite:

@misc{fattouhi2025imdb,
  author = {Fattouhi, Radwa and Boktaya, Amine},
  title = {LSTM IMDB Sentiment Analysis: Transfer Learning vs Traditional Approaches},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/radwa-f/DeBERTA-Imdb-SentimentAnalysis}}
  howpublished = {\url{https://huggingface.co/radwa-f/LSTM-IMDB-SentimentAnalysis}}
}

Related Work

Published Research:

AgriAlertX: Climate-driven disaster prevention for agriculture
Journal: SoftwareX (Elsevier)
DOI: 10.1016/j.softx.2025.102350

Related Projects:

Riot Detection System (DeBERTa for social media classification)
Tweet Detoxification (BART for style transfer)

Author

Radwa Fattouhi - ENSA El Jadida, Morocco

Downloads last month: -

radwa-f
/

LSTM-IMDB-SentimentAnalysis