Sentiment analysis deep learning model based on a novel hybrid embedding method

(WE) are crucial for capturing the meanings of words, offering continuous vector representations that encode both semantic and syntactic information. In this paper, we present a novel approach called WordFast, which combines the strengths of FastText and Word2Vec through a linear combination method. The WordFast approach aims to enhance the performance of WE, particularly in the context of sentiment analysis (SA). SA has become a prominent area of research in Natural Language Processing (NLP), especially when it comes to analyzing user opinions on digital platforms. Our proposed (SA) deep model is based on the WordFast method and incorporates two variations of Recurrent Neural Network (RNN) architectures. This model is tested using two datasets: IMDB reviews and Amazon reviews.The outcomes produced by the WordFast method are classified using Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) models.Our experiments reveal a significant improvement in accuracy when analyzing real IMDB, achieving 88.75/% and 89.54%, as well as real Amazon reviews, with accuracies of 94.69% and 94.89%.