This repository contains a Natural Language Processing (NLP) model based on Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) architecture. The model is designed to classify movie reviews from the IMDB dataset and assign a percentage score representing the sentiment (the closer to 100%, the more positive the review).
The model is trained on the IMDB dataset, which consists of a large collection of movie reviews labeled as positive or negative. The dataset is widely used for sentiment analysis tasks in NLP.
The NLP model utilizes the power of LSTM cells to capture long-term dependencies in the text data. LSTM is a type of RNN that can retain information over longer sequences, making it well-suited for sentiment analysis tasks. The model is trained using a combination of word embeddings, LSTM layers, and dense layers for classification.